system

Abstract

The system according to this embodiment aims to provide optimal sightseeing tours based on the user's interests and destinations. [Solution] The system according to the embodiment comprises a reception unit, an analysis unit, a generation unit, and a display unit. The reception unit receives input from the user regarding their travel destination and interests. The analysis unit analyzes the group's situation based on the information input by the reception unit. The generation unit generates an appropriate sightseeing tour based on the results analyzed by the analysis unit. The display unit displays the tour information generated by the generation unit, overlaid on the real world.

Description

Technical Field

[0004] ,

[0006] , , , ,

[0005] , , , , ,

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, and includes steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a character of the chatbot, 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, it has not been fully achieved to provide an optimal tourism tour based on the interests and destinations of users, and there is room for improvement.

[0005] The system according to the embodiment aims to provide an optimal tourism tour based on the interests and destinations of users.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a reception unit, an analysis unit, a generation unit, and a display unit. The reception unit receives input from the user regarding their travel destination and interests. The analysis unit analyzes the group's situation based on the information entered by the reception unit. The generation unit generates an appropriate sightseeing tour based on the results of the analysis performed by the analysis unit. The display unit overlays the tour information generated by the generation unit onto the real world. [Effects of the Invention]

[0007] The system according to this embodiment can provide optimal sightseeing tours based on the user's interests and destinations. [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 applied to the communication I / F include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

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

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

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

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

[0019] The smart device 14 comprises a computer 36, a reception 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 reception 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 sightseeing tour provision system according to an embodiment of the present invention is a system that provides optimal sightseeing tours for groups of travelers visiting locations featured in anime and dramas, utilizing generative AI and AR. This sightseeing tour provision system is compatible with the "Tourism DX" promoted by the Japan Tourism Agency and solves the problem that the enjoyment of conventional sightseeing tours and stamp rallies diminishes after being played once. First, the system takes the user's travel destination and interests as input. Next, the system's generative AI analyzes the group's situation (interests, number of times participating, experience, etc.) and generates an optimal sightseeing tour. The generated tour is displayed overlaid on the real world using AR technology. This allows users to enjoy the sightseeing experience at the location more. For example, when visiting a location featured in an anime, the system's generative AI provides information and episodes related to that anime, and characters appear through AR. The system can also suggest different routes and activities based on the group's interests and experience. This enables sightseeing tours that offer new experiences every time. Furthermore, the system collects user feedback and reflects it in the next tour to provide a more personalized experience. For example, it can suggest revisiting spots that were particularly popular on the previous tour. This system ensures that the enjoyment of sightseeing tours isn't limited to a single experience, but can be enjoyed repeatedly with new experiences. Furthermore, by providing optimal tours tailored to the group's needs, the system can improve traveler satisfaction. In this way, the sightseeing tour provision system can enhance the user's sightseeing experience.

[0029] The sightseeing tour provision system according to this embodiment comprises a reception unit, an analysis unit, a generation unit, and a display unit. The reception unit receives input from the user regarding their travel destination and interests. The user's travel destination and interests include, but are not limited to, tourist attractions, activities, and food preferences. The reception unit provides, for example, an interface for the user to input their travel destination and interests. The analysis unit analyzes the group's characteristics based on the information entered by the reception unit. The group's characteristics include, for example, age groups, number of people, and common interests. The analysis unit collects and analyzes data such as the user's interests, number of times participating, and experience. The generation unit generates an optimal sightseeing tour based on the results analyzed by the analysis unit. The generation unit generates an appropriate sightseeing tour based on the analyzed data. An appropriate sightseeing tour includes, for example, the tour content, duration, and budget. The generation unit collects, for example, user feedback and incorporates it into future sightseeing tours. The display unit displays the tour information generated by the generation unit overlaid on the real world. The display unit, for example, overlays tour information generated using AR technology onto the real world. This allows the tourism tour provision system according to this embodiment to enhance the user's tourism experience.

[0030] The analysis unit can collect and analyze data on users' interests, participation frequency, and experiences. For example, the analysis unit can collect and analyze data on users' past travel destinations, activities participated in, and travel frequency. The analysis unit can also suggest optimal sightseeing tours based on users' interests and experiences. For example, the analysis unit can suggest different routes and activities based on users' interests and experiences. In this way, the analysis unit can provide more personalized sightseeing tours by collecting and analyzing data such as users' interests, participation frequency, and experiences. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit can input data on users' interests, participation frequency, and experiences into a generative AI and obtain analysis results from the generative AI.

[0031] The generation unit can generate appropriate sightseeing tours based on the analyzed data. For example, the generation unit can generate appropriate sightseeing tours based on the analyzed data. For example, the generation unit can generate tours tailored to the user's interests and experience based on the analyzed data. The generation unit can also suggest different routes and activities based on the analyzed data. For example, the generation unit can suggest different routes and activities based on the user's interests and experience. In this way, the generation unit can provide tours tailored to the user's interests and experience by generating the optimal sightseeing tour based on the analyzed data. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input the analyzed data into a generation AI and obtain the optimal sightseeing tour from the generation AI.

[0032] The display unit can overlay the generated tour information onto the real world. For example, the display unit can overlay the generated tour information onto the real world. For example, the display unit can overlay the generated tour information onto the real world using AR technology. The display unit can also visually display the generated tour information. For example, the display unit can visually display the generated tour information and provide it to the user. In this way, the display unit enhances the user's sightseeing experience by overlaying the generated tour information onto the real world. Some or all of the above processing in the display unit may be performed using AR technology, for example, or without using AR technology. For example, the display unit can display the generated tour information using AR technology and provide it to the user.

[0033] The generation unit can collect user feedback and incorporate it into the next sightseeing tour. For example, the generation unit can collect user feedback and incorporate it into the next sightseeing tour. For example, the generation unit can improve the next sightseeing tour based on user feedback. The generation unit can also adjust the content of the next sightseeing tour based on user feedback. For example, the generation unit can adjust the content of the next sightseeing tour based on user feedback. In this way, the generation unit can provide a more personalized experience by collecting user feedback and incorporating it into the next sightseeing tour. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input user feedback into a generation AI and obtain suggestions for improvement for the next sightseeing tour from the generation AI.

[0034] The display unit can provide a character appearance effect via AR. For example, the display unit can provide a character appearance effect via AR. For example, the display unit can provide a character appearance effect using AR technology. The display unit can also visually display the character appearance effect. For example, the display unit can visually display and provide the character appearance effect to the user. In this way, the display unit enhances the user's sightseeing experience by providing a character appearance effect via AR. Some or all of the above processing in the display unit may be performed using AR technology, or without using AR technology. For example, the display unit can display and provide the user with a character appearance effect using AR technology.

[0035] The reception desk can analyze the user's past travel history and select an appropriate input method. For example, the reception desk can analyze the user's past travel history and select an appropriate input method. For example, the reception desk can automatically display places the user has visited in the past as suggestions. The reception desk can also prioritize suggesting input methods (voice, text, etc.) that the user has used in the past. Furthermore, the reception desk can predict and suggest places the user might visit at a specific time based on their past travel history. In this way, the reception desk can provide a more appropriate input method by analyzing the user's past travel history. Some or all of the above processing in the reception desk may be performed using, for example, a generative AI, or not using a generative AI. For example, the reception desk can input the user's past travel history into a generative AI and obtain the optimal input method from the generative AI.

[0036] The reception unit can perform filtering based on the user's current lifestyle and areas of interest when the user inputs their travel destination and interests. For example, the reception unit can suggest relevant travel destinations based on the user's current lifestyle. The reception unit can also suggest activities of interest based on the user's areas of interest. Furthermore, the reception unit can narrow down the list of potential travel destinations based on the user's current lifestyle and areas of interest. In this way, the reception unit can suggest more appropriate travel destinations and activities by filtering based on the user's current lifestyle and areas of interest. Some or all of the above processing in the reception unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the reception unit can input data on the user's current lifestyle and areas of interest into a generative AI and obtain filtering results from the generative AI.

[0037] The reception desk can prioritize inputting highly relevant information by considering the user's geographical location when the user inputs their travel destination and interests. For example, the reception desk can prioritize suggesting tourist destinations close to the user's current location. The reception desk can also suggest relevant activities based on the user's geographical location. Furthermore, the reception desk can suggest the most suitable travel destination by considering the user's geographical location. In this way, the reception desk can provide more relevant information by considering the user's geographical location. Some or all of the above processing in the reception desk may be performed using, for example, a generative AI, or without a generative AI. For example, the reception desk can input the user's geographical location into a generative AI and obtain highly relevant information from the generative AI.

[0038] The reception desk can analyze the user's social media activity and input relevant information when the user enters their travel destination and interests. For example, the reception desk can suggest tourist destinations of interest based on the user's social media activity. The reception desk can also suggest relevant activities based on the user's social media activity. Furthermore, the reception desk can analyze the user's social media activity and suggest the most suitable travel destination. In this way, the reception desk can provide more relevant information by analyzing the user's social media activity. Some or all of the above processing in the reception desk may be performed using, for example, a generative AI, or not using a generative AI. For example, the reception desk can input data on the user's social media activity into a generative AI and obtain relevant information from the generative AI.

[0039] The analysis unit can improve the accuracy of its analysis by considering the user's past travel history during the analysis process. For example, the analysis unit can improve the accuracy of its analysis by considering the user's past travel history during the analysis process. For example, the analysis unit can perform the optimal analysis based on the user's past travel history. The analysis unit can also perform an analysis that avoids congestion based on the user's past travel history. Furthermore, the analysis unit can analyze the user's past travel history and perform the most efficient analysis. As a result, the analysis unit improves the accuracy of its analysis by considering the user's past travel history. Some or all of the above processing in the analysis unit may be performed using, for example, a generating AI, or without using a generating AI. For example, the analysis unit can input the user's past travel history into a generating AI and obtain results from the generating AI to improve the accuracy of the analysis.

[0040] The analysis unit can perform analysis while considering the user's attribute information. For example, the analysis unit can perform analysis based on the user's age and gender. It can also perform analysis based on the user's occupation and hobbies. Furthermore, it can perform analysis based on the user's family structure and lifestyle. This allows the analysis unit to perform more appropriate analysis by considering the user's attribute information. Some or all of the above processing in the analysis unit may be performed using, for example, a generating AI, or without using a generating AI. For example, the analysis unit can input the user's attribute information into a generating AI and obtain the analysis results from the generating AI.

[0041] The analysis unit can perform analysis while considering the geographical distribution of users. For example, the analysis unit can perform analysis based on the user's current location. It can also perform analysis based on the user's past visited locations. Furthermore, the analysis unit can perform optimal analysis while considering the geographical distribution of users. This allows the analysis unit to perform more appropriate analysis by considering the geographical distribution of users. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit can input data on the geographical distribution of users into a generative AI and obtain analysis results from the generative AI.

[0042] The analysis unit can improve the accuracy of its analysis by referring to relevant literature and data during the analysis process. For example, the analysis unit can improve the accuracy of its analysis by referring to relevant academic papers. The analysis unit can also perform analysis by referring to relevant databases. The analysis unit can also perform analysis by referring to relevant statistical data. As a result, the analysis unit improves the accuracy of its analysis by referring to relevant literature and data. Some or all of the above-described processes in the analysis unit may be performed using, for example, a generating AI, or without using a generating AI. For example, the analysis unit can input relevant literature and data into a generating AI and obtain analysis results from the generating AI.

[0043] The generation unit can generate appropriate tours by analyzing the user's past travel history during the generation process. For example, the generation unit can generate the optimal tour based on the user's past travel history. The generation unit can also generate tours that avoid crowds based on the user's past travel history. Furthermore, the generation unit can analyze the user's past travel history and generate the most efficient tour. In this way, the generation unit can provide more appropriate tours by analyzing the user's past travel history. Some or all of the above processing in the generation unit may be performed using, for example, a generation AI, or without using a generation AI. For example, the generation unit can input the user's past travel history into a generation AI and obtain results from the generation AI for generating the optimal tour.

[0044] The generation unit can customize the tour content based on the user's current lifestyle during the generation process. For example, the generation unit can customize the tour content based on the user's current lifestyle during the generation process. For example, the generation unit can generate a tour that includes relevant activities based on the user's current lifestyle. The generation unit can also generate a tour that includes the most suitable travel destination based on the user's current lifestyle. Furthermore, the generation unit can narrow down the list of potential travel destinations based on the user's current lifestyle. This allows the generation unit to provide a more appropriate tour by customizing the tour content based on the user's current lifestyle. Some or all of the above processing in the generation unit may be performed using, for example, a generation AI, or without using a generation AI. For example, the generation unit can input data on the user's current lifestyle into the generation AI and obtain results from the generation AI for customizing the tour content.

[0045] The generation unit can generate an appropriate tour by considering the user's geographical location information during the generation process. For example, the generation unit can generate the optimal tour based on the user's current location. The generation unit can also generate the optimal tour based on the user's past visited locations. Furthermore, the generation unit can generate the optimal tour by considering the user's geographical location information. This allows the generation unit to provide a more appropriate tour by considering the user's geographical location information. Some or all of the above-described processes in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input the user's geographical location information into the generation AI and obtain the result for generating the optimal tour from the generation AI.

[0046] The generation unit can analyze the user's social media activity during generation and propose tour content. For example, the generation unit can analyze the user's social media activity during generation and propose tour content. For example, the generation unit can propose tourist destinations of interest based on the user's social media activity. The generation unit can also propose related activities based on the user's social media activity. Furthermore, the generation unit can analyze the user's social media activity and propose the most suitable travel destination. In this way, the generation unit can provide more appropriate tours by analyzing the user's social media activity. Some or all of the above processing in the generation unit may be performed using, for example, a generation AI, or without a generation AI. For example, the generation unit can input data on the user's social media activity into a generation AI and obtain results from the generation AI for proposing tour content.

[0047] The display unit can select the optimal display method by referring to the user's past operation history when displaying information. For example, the display unit can select the optimal display method by referring to the user's past operation history when displaying information. For example, the display unit can provide the optimal display method based on the user's past operation history. The display unit can also provide a highly visible display method from the user's past operation history. Furthermore, the display unit can analyze the user's past operation history and provide the most efficient display method. In this way, the display unit can provide a more appropriate display method by referring to the user's past operation history. Some or all of the above processing in the display unit may be performed using, for example, a generation AI, or without using a generation AI. For example, the display unit can input data of the user's past operation history into a generation AI and obtain the optimal display method from the generation AI.

[0048] The display unit can perform display operations while considering the user's attribute information. For example, the display unit can perform display operations while considering the user's attribute information. For example, the display unit can provide an optimal display method based on the user's age and gender. The display unit can also display relevant information based on the user's occupation and hobbies. Furthermore, the display unit can provide an optimal display method based on the user's family structure and lifestyle. As a result, the display unit can perform more appropriate displays by considering the user's attribute information. Some or all of the above processing in the display unit may be performed using, for example, a generation AI, or without a generation AI. For example, the display unit can input user attribute information data into a generation AI and obtain an optimal display method from the generation AI.

[0049] The display unit can select the optimal display method when displaying information, taking into account the user's geographical location. For example, the display unit can select the optimal display method when displaying information, taking into account the user's geographical location. For example, the display unit can provide the optimal display method based on the user's current location. The display unit can also provide the optimal display method based on the user's past visited locations. Furthermore, the display unit can provide the optimal display method by taking into account the user's geographical location. In this way, the display unit can provide a more appropriate display method by taking into account the user's geographical location. Some or all of the above processing in the display unit may be performed using, for example, a generation AI, or without using a generation AI. For example, the display unit can input the user's geographical location data into a generation AI and obtain the optimal display method from the generation AI.

[0050] The display unit can analyze the user's social media activity and adjust the displayed content at the time of display. For example, the display unit can analyze the user's social media activity and adjust the displayed content at the time of display. For example, the display unit can prioritize displaying information of interest to the user based on their social media activity. The display unit can also display relevant information based on the user's social media activity. Furthermore, the display unit can analyze the user's social media activity and provide optimal displayed content. In this way, the display unit can provide more appropriate information by analyzing the user's social media activity. Some or all of the above processing in the display unit may be performed using, for example, a generative AI, or without a generative AI. For example, the display unit can input data on the user's social media activity into a generative AI and obtain results from the generative AI to adjust the displayed content.

[0051] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0052] The reception desk can analyze the user's past travel history and select an appropriate input method. For example, the reception desk can automatically display places the user has visited in the past as suggestions. The reception desk can also prioritize suggesting input methods (voice, text, etc.) that the user has used in the past. Furthermore, the reception desk can predict and suggest places the user might visit at a specific time based on their past travel history. In this way, the reception desk can provide a more appropriate input method by analyzing the user's past travel history. Some or all of the above processing in the reception desk may be performed using, for example, a generative AI, or not using a generative AI. For example, the reception desk can input the user's past travel history into a generative AI and obtain the optimal input method from the generative AI.

[0053] The analysis unit can improve the accuracy of its analysis by considering the user's past travel history. For example, the analysis unit can perform an optimal analysis based on the user's past travel history. The analysis unit can also perform an analysis that avoids congestion based on the user's past travel history. Furthermore, the analysis unit can analyze the user's past travel history and perform the most efficient analysis. As a result, the analysis unit improves the accuracy of its analysis by considering the user's past travel history. Some or all of the above processing in the analysis unit may be performed using, for example, a generating AI, or without a generating AI. For example, the analysis unit can input the user's past travel history into a generating AI and obtain results from the generating AI to improve the accuracy of the analysis.

[0054] The generation unit can generate appropriate tours by analyzing the user's past travel history during the generation process. For example, the generation unit can generate the optimal tour based on the user's past travel history. The generation unit can also generate tours that avoid crowds based on the user's past travel history. Furthermore, the generation unit can analyze the user's past travel history and generate the most efficient tour. In this way, the generation unit can provide more appropriate tours by analyzing the user's past travel history. Some or all of the above processing in the generation unit may be performed using, for example, a generation AI, or without a generation AI. For example, the generation unit can input the user's past travel history into a generation AI and obtain results from the generation AI to generate the optimal tour.

[0055] The display unit can select the optimal display method by referring to the user's past operation history when displaying information. For example, the display unit can provide the optimal display method based on the user's past operation history. The display unit can also provide a highly visible display method based on the user's past operation history. Furthermore, the display unit can analyze the user's past operation history and provide the most efficient display method. In this way, the display unit can provide a more appropriate display method by referring to the user's past operation history. Some or all of the above processing in the display unit may be performed using, for example, a generation AI, or without a generation AI. For example, the display unit can input data of the user's past operation history into a generation AI and obtain the optimal display method from the generation AI.

[0056] The reception desk can prioritize inputting highly relevant information by considering the user's geographical location when the user inputs their travel destination and interests. For example, the reception desk can prioritize suggesting tourist destinations close to the user's current location. The reception desk can also suggest relevant activities based on the user's geographical location. Furthermore, the reception desk can suggest the most suitable travel destination by considering the user's geographical location. In this way, the reception desk can provide more relevant information by considering the user's geographical location. Some or all of the above processing in the reception desk may be performed using, for example, a generative AI, or without a generative AI. For example, the reception desk can input the user's geographical location into a generative AI and obtain highly relevant information from the generative AI.

[0057] The following briefly describes the processing flow for example form 1.

[0058] Step 1: The reception desk inputs the user's travel destination and interests. These include tourist attractions, activities, and food preferences. The reception desk provides an interface for the user to input their travel destination and interests. Step 2: The analysis unit analyzes the group's characteristics based on the information entered by the reception unit. These characteristics include age group, number of participants, and common interests. The analysis unit collects and analyzes data such as user interests, participation frequency, and experience. Step 3: The generation unit generates the optimal sightseeing tour based on the results analyzed by the analysis unit. An appropriate sightseeing tour includes the tour content, duration, budget, etc. The generation unit collects user feedback and incorporates it into future sightseeing tours. Step 4: The display unit overlays the tour information generated by the generation unit onto the real world. The display unit overlays the tour information generated using AR technology onto the real world.

[0059] (Example of form 2) The sightseeing tour provision system according to an embodiment of the present invention is a system that provides optimal sightseeing tours for groups of travelers visiting locations featured in anime and dramas, utilizing generative AI and AR. This sightseeing tour provision system is compatible with the "Tourism DX" promoted by the Japan Tourism Agency and solves the problem that the enjoyment of conventional sightseeing tours and stamp rallies diminishes after being played once. First, the system takes the user's travel destination and interests as input. Next, the system's generative AI analyzes the group's situation (interests, number of times participating, experience, etc.) and generates an optimal sightseeing tour. The generated tour is displayed overlaid on the real world using AR technology. This allows users to enjoy the sightseeing experience at the location more. For example, when visiting a location featured in an anime, the system's generative AI provides information and episodes related to that anime, and characters appear through AR. The system can also suggest different routes and activities based on the group's interests and experience. This enables sightseeing tours that offer new experiences every time. Furthermore, the system collects user feedback and reflects it in the next tour to provide a more personalized experience. For example, it can suggest revisiting spots that were particularly popular on the previous tour. This system ensures that the enjoyment of sightseeing tours isn't limited to a single experience, but can be enjoyed repeatedly with new experiences. Furthermore, by providing optimal tours tailored to the group's needs, the system can improve traveler satisfaction. In this way, the sightseeing tour provision system can enhance the user's sightseeing experience.

[0060] The sightseeing tour provision system according to this embodiment comprises a reception unit, an analysis unit, a generation unit, and a display unit. The reception unit receives input from the user regarding their travel destination and interests. The user's travel destination and interests include, but are not limited to, tourist attractions, activities, and food preferences. The reception unit provides, for example, an interface for the user to input their travel destination and interests. The analysis unit analyzes the group's characteristics based on the information entered by the reception unit. The group's characteristics include, for example, age groups, number of people, and common interests. The analysis unit collects and analyzes data such as the user's interests, number of times participating, and experience. The generation unit generates an optimal sightseeing tour based on the results analyzed by the analysis unit. The generation unit generates an appropriate sightseeing tour based on the analyzed data. An appropriate sightseeing tour includes, for example, the tour content, duration, and budget. The generation unit collects, for example, user feedback and incorporates it into future sightseeing tours. The display unit displays the tour information generated by the generation unit overlaid on the real world. The display unit, for example, overlays tour information generated using AR technology onto the real world. This allows the tourism tour provision system according to this embodiment to enhance the user's tourism experience.

[0061] The analysis unit can collect and analyze data on users' interests, participation frequency, and experiences. For example, the analysis unit can collect and analyze data on users' past travel destinations, activities participated in, and travel frequency. The analysis unit can also suggest optimal sightseeing tours based on users' interests and experiences. For example, the analysis unit can suggest different routes and activities based on users' interests and experiences. In this way, the analysis unit can provide more personalized sightseeing tours by collecting and analyzing data such as users' interests, participation frequency, and experiences. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit can input data on users' interests, participation frequency, and experiences into a generative AI and obtain analysis results from the generative AI.

[0062] The generation unit can generate appropriate sightseeing tours based on the analyzed data. For example, the generation unit can generate appropriate sightseeing tours based on the analyzed data. For example, the generation unit can generate tours tailored to the user's interests and experience based on the analyzed data. The generation unit can also suggest different routes and activities based on the analyzed data. For example, the generation unit can suggest different routes and activities based on the user's interests and experience. In this way, the generation unit can provide tours tailored to the user's interests and experience by generating the optimal sightseeing tour based on the analyzed data. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input the analyzed data into a generation AI and obtain the optimal sightseeing tour from the generation AI.

[0063] The display unit can overlay the generated tour information onto the real world. For example, the display unit can overlay the generated tour information onto the real world. For example, the display unit can overlay the generated tour information onto the real world using AR technology. The display unit can also visually display the generated tour information. For example, the display unit can visually display the generated tour information and provide it to the user. In this way, the display unit enhances the user's sightseeing experience by overlaying the generated tour information onto the real world. Some or all of the above processing in the display unit may be performed using AR technology, for example, or without using AR technology. For example, the display unit can display the generated tour information using AR technology and provide it to the user.

[0064] The generation unit can collect user feedback and incorporate it into the next sightseeing tour. For example, the generation unit can collect user feedback and incorporate it into the next sightseeing tour. For example, the generation unit can improve the next sightseeing tour based on user feedback. The generation unit can also adjust the content of the next sightseeing tour based on user feedback. For example, the generation unit can adjust the content of the next sightseeing tour based on user feedback. In this way, the generation unit can provide a more personalized experience by collecting user feedback and incorporating it into the next sightseeing tour. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input user feedback into a generation AI and obtain suggestions for improvement for the next sightseeing tour from the generation AI.

[0065] The display unit can provide a character appearance effect via AR. For example, the display unit can provide a character appearance effect via AR. For example, the display unit can provide a character appearance effect using AR technology. The display unit can also visually display the character appearance effect. For example, the display unit can visually display and provide the character appearance effect to the user. In this way, the display unit enhances the user's sightseeing experience by providing a character appearance effect via AR. Some or all of the above processing in the display unit may be performed using AR technology, or without using AR technology. For example, the display unit can display and provide the user with a character appearance effect using AR technology.

[0066] The reception desk can estimate the user's emotions and adjust the input method for travel destinations and interests based on the estimated emotions. For example, if the user is excited, the reception desk may provide detailed input options and suggest a customizable input method. If the user is relaxed, the reception desk may also provide a simple interface and minimize the input steps. If the user is stressed, the reception desk may prioritize voice input to allow for quick input of travel destinations and interests. This allows the reception desk to provide more appropriate input by adjusting the input method according to 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 reception desk may be performed using or without generative AI. For example, the reception desk can input user emotion data into a generating AI and obtain adjustments to the input method based on that emotion from the generating AI.

[0067] The reception desk can analyze the user's past travel history and select an appropriate input method. For example, the reception desk can analyze the user's past travel history and select an appropriate input method. For example, the reception desk can automatically display places the user has visited in the past as suggestions. The reception desk can also prioritize suggesting input methods (voice, text, etc.) that the user has used in the past. Furthermore, the reception desk can predict and suggest places the user might visit at a specific time based on their past travel history. In this way, the reception desk can provide a more appropriate input method by analyzing the user's past travel history. Some or all of the above processing in the reception desk may be performed using, for example, a generative AI, or not using a generative AI. For example, the reception desk can input the user's past travel history into a generative AI and obtain the optimal input method from the generative AI.

[0068] The reception unit can perform filtering based on the user's current lifestyle and areas of interest when the user inputs their travel destination and interests. For example, the reception unit can suggest relevant travel destinations based on the user's current lifestyle. The reception unit can also suggest activities of interest based on the user's areas of interest. Furthermore, the reception unit can narrow down the list of potential travel destinations based on the user's current lifestyle and areas of interest. In this way, the reception unit can suggest more appropriate travel destinations and activities by filtering based on the user's current lifestyle and areas of interest. Some or all of the above processing in the reception unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the reception unit can input data on the user's current lifestyle and areas of interest into a generative AI and obtain filtering results from the generative AI.

[0069] The reception unit can estimate the user's emotions and determine the priority of the information to be entered based on the estimated emotions. For example, if the user is excited, the reception unit may prioritize detailed information. If the user is relaxed, the reception unit may also prioritize basic information. If the user is stressed, the reception unit may also prioritize the most important information. This allows the reception unit to input more appropriate information by prioritizing the information to be entered according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the reception unit can input user emotion data into a generative AI and obtain results from the generative AI to determine the priority of information.

[0070] The reception desk can prioritize inputting highly relevant information by considering the user's geographical location when the user inputs their travel destination and interests. For example, the reception desk can prioritize suggesting tourist destinations close to the user's current location. The reception desk can also suggest relevant activities based on the user's geographical location. Furthermore, the reception desk can suggest the most suitable travel destination by considering the user's geographical location. In this way, the reception desk can provide more relevant information by considering the user's geographical location. Some or all of the above processing in the reception desk may be performed using, for example, a generative AI, or without a generative AI. For example, the reception desk can input the user's geographical location into a generative AI and obtain highly relevant information from the generative AI.

[0071] The reception desk can analyze the user's social media activity and input relevant information when the user enters their travel destination and interests. For example, the reception desk can suggest tourist destinations of interest based on the user's social media activity. The reception desk can also suggest relevant activities based on the user's social media activity. Furthermore, the reception desk can analyze the user's social media activity and suggest the most suitable travel destination. In this way, the reception desk can provide more relevant information by analyzing the user's social media activity. Some or all of the above processing in the reception desk may be performed using, for example, a generative AI, or not using a generative AI. For example, the reception desk can input data on the user's social media activity into a generative AI and obtain relevant information from the generative AI.

[0072] The analysis unit can estimate the user's emotions and adjust the analysis criteria based on the estimated emotions. For example, the analysis unit can perform a detailed analysis if the user is excited. It can also perform a basic analysis if the user is relaxed. It can also perform an analysis based on the most important information if the user is stressed. This allows the analysis unit to perform a more appropriate analysis by adjusting the analysis criteria according to 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-described processes in the analysis unit may be performed using a generative AI, or not. For example, the analysis unit can input user emotion data into a generative AI and obtain results from the generative AI to adjust the analysis criteria.

[0073] The analysis unit can improve the accuracy of its analysis by considering the user's past travel history during the analysis process. For example, the analysis unit can improve the accuracy of its analysis by considering the user's past travel history during the analysis process. For example, the analysis unit can perform the optimal analysis based on the user's past travel history. The analysis unit can also perform an analysis that avoids congestion based on the user's past travel history. Furthermore, the analysis unit can analyze the user's past travel history and perform the most efficient analysis. As a result, the analysis unit improves the accuracy of its analysis by considering the user's past travel history. Some or all of the above processing in the analysis unit may be performed using, for example, a generating AI, or without using a generating AI. For example, the analysis unit can input the user's past travel history into a generating AI and obtain results from the generating AI to improve the accuracy of the analysis.

[0074] The analysis unit can perform analysis while considering the user's attribute information. For example, the analysis unit can perform analysis based on the user's age and gender. It can also perform analysis based on the user's occupation and hobbies. Furthermore, it can perform analysis based on the user's family structure and lifestyle. This allows the analysis unit to perform more appropriate analysis by considering the user's attribute information. Some or all of the above processing in the analysis unit may be performed using, for example, a generating AI, or without using a generating AI. For example, the analysis unit can input the user's attribute information into a generating AI and obtain the analysis results from the generating AI.

[0075] The analysis unit can estimate the user's emotions and adjust the display order of the analysis results based on the estimated emotions. For example, if the user is excited, the analysis unit may prioritize displaying detailed analysis results. If the user is relaxed, the analysis unit may also prioritize displaying basic analysis results. If the user is stressed, the analysis unit may also prioritize displaying the most important analysis results. In this way, the analysis unit can provide more appropriate information by adjusting the display order of analysis results according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using a generative AI, or not. For example, the analysis unit can input user emotion data into a generative AI and obtain results from the generative AI to adjust the display order of the analysis results.

[0076] The analysis unit can perform analysis while considering the geographical distribution of users. For example, the analysis unit can perform analysis based on the user's current location. It can also perform analysis based on the user's past visited locations. Furthermore, the analysis unit can perform optimal analysis while considering the geographical distribution of users. This allows the analysis unit to perform more appropriate analysis by considering the geographical distribution of users. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit can input data on the geographical distribution of users into a generative AI and obtain analysis results from the generative AI.

[0077] The analysis unit can improve the accuracy of its analysis by referring to relevant literature and data during the analysis process. For example, the analysis unit can improve the accuracy of its analysis by referring to relevant academic papers. The analysis unit can also perform analysis by referring to relevant databases. The analysis unit can also perform analysis by referring to relevant statistical data. As a result, the analysis unit improves the accuracy of its analysis by referring to relevant literature and data. Some or all of the above-described processes in the analysis unit may be performed using, for example, a generating AI, or without using a generating AI. For example, the analysis unit can input relevant literature and data into a generating AI and obtain analysis results from the generating AI.

[0078] The generation unit can estimate the user's emotions and adjust the content of the generated tour based on the estimated emotions. For example, if the user is excited, the generation unit can generate a tour that includes active activities. If the user is relaxed, the generation unit can also generate a tour that includes relaxing activities. If the user is stressed, the generation unit can also generate a tour that includes stress-relieving activities. In this way, the generation unit can provide a more appropriate tour by adjusting the tour content according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input user emotion data into a generation AI and obtain results from the generation AI to adjust the tour content.

[0079] The generation unit can generate appropriate tours by analyzing the user's past travel history during the generation process. For example, the generation unit can generate the optimal tour based on the user's past travel history. The generation unit can also generate tours that avoid crowds based on the user's past travel history. Furthermore, the generation unit can analyze the user's past travel history and generate the most efficient tour. In this way, the generation unit can provide more appropriate tours by analyzing the user's past travel history. Some or all of the above processing in the generation unit may be performed using, for example, a generation AI, or without using a generation AI. For example, the generation unit can input the user's past travel history into a generation AI and obtain results from the generation AI for generating the optimal tour.

[0080] The generation unit can customize the tour content based on the user's current lifestyle during the generation process. For example, the generation unit can customize the tour content based on the user's current lifestyle during the generation process. For example, the generation unit can generate a tour that includes relevant activities based on the user's current lifestyle. The generation unit can also generate a tour that includes the most suitable travel destination based on the user's current lifestyle. Furthermore, the generation unit can narrow down the list of potential travel destinations based on the user's current lifestyle. This allows the generation unit to provide a more appropriate tour by customizing the tour content based on the user's current lifestyle. Some or all of the above processing in the generation unit may be performed using, for example, a generation AI, or without using a generation AI. For example, the generation unit can input data on the user's current lifestyle into the generation AI and obtain results from the generation AI for customizing the tour content.

[0081] The generation unit can estimate the user's emotions and determine the priority of the tours to generate based on the estimated emotions. For example, if the user is excited, the generation unit can generate tours that prioritize active activities. If the user is relaxed, the generation unit can also generate tours that prioritize relaxing activities. If the user is stressed, the generation unit can also generate tours that prioritize stress-relieving activities. In this way, the generation unit can provide more appropriate tours by prioritizing tours according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI 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 generation unit may be performed using a generation AI, or not. For example, the generation unit can input user emotion data into a generation AI and obtain results from the generation AI to determine tour priorities.

[0082] The generation unit can generate an appropriate tour by considering the user's geographical location information during the generation process. For example, the generation unit can generate the optimal tour based on the user's current location. The generation unit can also generate the optimal tour based on the user's past visited locations. Furthermore, the generation unit can generate the optimal tour by considering the user's geographical location information. This allows the generation unit to provide a more appropriate tour by considering the user's geographical location information. Some or all of the above-described processes in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input the user's geographical location information into the generation AI and obtain the result for generating the optimal tour from the generation AI.

[0083] The generation unit can analyze the user's social media activity during generation and propose tour content. For example, the generation unit can analyze the user's social media activity during generation and propose tour content. For example, the generation unit can propose tourist destinations of interest based on the user's social media activity. The generation unit can also propose related activities based on the user's social media activity. Furthermore, the generation unit can analyze the user's social media activity and propose the most suitable travel destination. In this way, the generation unit can provide more appropriate tours by analyzing the user's social media activity. Some or all of the above processing in the generation unit may be performed using, for example, a generation AI, or without a generation AI. For example, the generation unit can input data on the user's social media activity into a generation AI and obtain results from the generation AI for proposing tour content.

[0084] The display unit can estimate the user's emotions and adjust the display method based on the estimated emotions. For example, the display unit can provide a visually stimulating display method when the user is excited. It can also provide a calming display method when the user is relaxed. It can also provide a simple and highly visible display method when the user is stressed. This allows the display unit to provide a more appropriate display by adjusting the display method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the display unit may be performed using a generative AI, or not. For example, the display unit can input user emotion data into a generative AI and obtain the results from the generative AI to adjust the display method.

[0085] The display unit can select the optimal display method by referring to the user's past operation history when displaying information. For example, the display unit can select the optimal display method by referring to the user's past operation history when displaying information. For example, the display unit can provide the optimal display method based on the user's past operation history. The display unit can also provide a highly visible display method from the user's past operation history. Furthermore, the display unit can analyze the user's past operation history and provide the most efficient display method. In this way, the display unit can provide a more appropriate display method by referring to the user's past operation history. Some or all of the above processing in the display unit may be performed using, for example, a generation AI, or without using a generation AI. For example, the display unit can input data of the user's past operation history into a generation AI and obtain the optimal display method from the generation AI.

[0086] The display unit can perform display operations while considering the user's attribute information. For example, the display unit can perform display operations while considering the user's attribute information. For example, the display unit can provide an optimal display method based on the user's age and gender. The display unit can also display relevant information based on the user's occupation and hobbies. Furthermore, the display unit can provide an optimal display method based on the user's family structure and lifestyle. As a result, the display unit can perform more appropriate displays by considering the user's attribute information. Some or all of the above processing in the display unit may be performed using, for example, a generation AI, or without a generation AI. For example, the display unit can input user attribute information data into a generation AI and obtain an optimal display method from the generation AI.

[0087] The display unit can estimate the user's emotions and determine the priority of the displayed content based on the estimated emotions. For example, if the user is excited, the display unit may prioritize displaying detailed information. If the user is relaxed, the display unit may prioritize displaying basic information. If the user is stressed, the display unit may prioritize displaying the most important information. In this way, the display unit can provide more appropriate information by prioritizing the displayed content according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The 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 display unit may be performed using a generative AI, or not. For example, the display unit can input user emotion data into a generative AI and obtain results from the generative AI to determine the priority of the displayed content.

[0088] The display unit can select the optimal display method when displaying information, taking into account the user's geographical location. For example, the display unit can select the optimal display method when displaying information, taking into account the user's geographical location. For example, the display unit can provide the optimal display method based on the user's current location. The display unit can also provide the optimal display method based on the user's past visited locations. Furthermore, the display unit can provide the optimal display method by taking into account the user's geographical location. In this way, the display unit can provide a more appropriate display method by taking into account the user's geographical location. Some or all of the above processing in the display unit may be performed using, for example, a generation AI, or without using a generation AI. For example, the display unit can input the user's geographical location data into a generation AI and obtain the optimal display method from the generation AI.

[0089] The display unit can analyze the user's social media activity and adjust the displayed content at the time of display. For example, the display unit can analyze the user's social media activity and adjust the displayed content at the time of display. For example, the display unit can prioritize displaying information of interest to the user based on their social media activity. The display unit can also display relevant information based on the user's social media activity. Furthermore, the display unit can analyze the user's social media activity and provide optimal displayed content. In this way, the display unit can provide more appropriate information by analyzing the user's social media activity. Some or all of the above processing in the display unit may be performed using, for example, a generative AI, or without a generative AI. For example, the display unit can input data on the user's social media activity into a generative AI and obtain results from the generative AI to adjust the displayed content. === Hard Collateral 1-1 === Each of the multiple elements described above, including the reception unit, analysis unit, generation unit, and display unit, is implemented, for example, in at least one of the smart device 14 and the data processing unit 12. For example, the reception unit is implemented by the reception device 38 of the smart device 14 and provides an interface for the user to input their travel destination and interests. The analysis unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12 and collects and analyzes data such as the user's interests, number of times participating, and experience. The generation unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12 and generates an appropriate sightseeing tour based on the analyzed data. The display unit is implemented, for example, by the output device 40 of the smart device 14 and displays the generated tour information overlaid on the real world using AR technology. === Hard Collateral 1-2 === Each of the multiple elements described above, including the reception unit, analysis unit, generation unit, and display unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing unit 12. For example, the reception unit is implemented by the microphone 238 of the smart glasses 214 and provides an interface for the user to input their travel destination and interests. The analysis unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12 and collects and analyzes data such as the user's interests, number of times participating, and experience. The generation unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12 and generates an appropriate sightseeing tour based on the analyzed data. The display unit is implemented, for example, by the speaker 240 of the smart glasses 214 and displays the generated tour information overlaid on the real world using AR technology. === Hard Collateral 1-3 === Each of the multiple elements described above, including the reception unit, analysis unit, generation unit, and display unit, is implemented, for example, by at least one of the headset terminal 314 and the data processing unit 12. For example, the reception unit is implemented by the microphone 238 of the headset terminal 314 and provides an interface for the user to input their travel destination and interests. The analysis unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12 and collects and analyzes data such as the user's interests, number of times participating, and experience. The generation unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12 and generates an appropriate sightseeing tour based on the analyzed data. The display unit is implemented, for example, by the display 343 of the headset terminal 314 and displays the generated tour information overlaid on the real world using AR technology. === Hard Collateral 1-4 === Each of the multiple elements described above, including the reception unit, analysis unit, generation unit, and display unit, is implemented, for example, by at least one of the robot 414 and the data processing unit 12. For example, the reception unit is implemented by the microphone 238 of the robot 414 and provides an interface for the user to input their travel destination and interests. The analysis unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12 and collects and analyzes data such as the user's interests, number of times participating, and experience. The generation unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12 and generates an appropriate sightseeing tour based on the analyzed data. The display unit is implemented, for example, by the speaker 240 of the robot 414 and displays the generated tour information overlaid on the real world using AR technology.

[0090] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0091] The analysis unit can estimate the user's emotions and adjust the analysis criteria based on the estimated emotions. For example, if the user is excited, the analysis unit can perform a detailed analysis. If the user is relaxed, the analysis unit can perform a basic analysis. If the user is stressed, the analysis unit can perform an analysis based on the most important information. This allows the analysis unit to perform a more appropriate analysis by adjusting the analysis criteria according to 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 analysis unit may be performed using a generative AI, or not. For example, the analysis unit can input user emotion data into a generative AI and obtain results from the generative AI to adjust the analysis criteria.

[0092] The generation unit can estimate the user's emotions and adjust the content of the generated tour based on the estimated emotions. For example, if the user is excited, the generation unit can generate a tour that includes active activities. If the user is relaxed, the generation unit can also generate a tour that includes relaxing activities. If the user is stressed, the generation unit can also generate a tour that includes stress-relieving activities. In this way, the generation unit can provide a more appropriate tour by adjusting the tour content according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI 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 generation unit may be performed using a generation AI, or not. For example, the generation unit can input user emotion data into a generation AI and obtain results from the generation AI to adjust the tour content.

[0093] The display unit can estimate the user's emotions and adjust the display method based on the estimated emotions. For example, if the user is excited, the display unit can provide a visually stimulating display method. It can also provide a calming display method if the user is relaxed. Furthermore, if the user is stressed, it can provide a simple and highly visible display method. This allows the display unit to provide a more appropriate display by adjusting the display method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the display unit may be performed using a generative AI, or not. For example, the display unit can input user emotion data into a generative AI and obtain results from the generative AI to adjust the display method.

[0094] The reception desk can estimate the user's emotions and adjust the input method for travel destinations and interests based on the estimated emotions. For example, if the user is excited, the reception desk can provide detailed input options and suggest a customizable input method. If the user is relaxed, the reception desk can also provide a simple interface and minimize the input steps. If the user is stressed, the reception desk can prioritize voice input to allow for quick input of travel destinations and interests. This allows the reception desk to provide more appropriate input by adjusting the input method according to 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 reception desk may be performed using or without a generative AI. For example, the reception desk can input user emotion data into a generative AI and obtain emotion-based adjustments to the input method from the generative AI.

[0095] The analysis unit can estimate the user's emotions and adjust the display order of the analysis results based on the estimated emotions. For example, if the user is excited, the analysis unit can prioritize displaying detailed analysis results. It can also prioritize displaying basic analysis results if the user is relaxed. Furthermore, if the user is stressed, it can prioritize displaying the most important analysis results. This allows the analysis unit to provide more appropriate information by adjusting the display order of analysis results according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the analysis unit may be performed using a generative AI, or not. For example, the analysis unit can input user emotion data into a generative AI and obtain results from the generative AI to adjust the display order of the analysis results.

[0096] The reception desk can analyze the user's past travel history and select an appropriate input method. For example, the reception desk can automatically display places the user has visited in the past as suggestions. The reception desk can also prioritize suggesting input methods (voice, text, etc.) that the user has used in the past. Furthermore, the reception desk can predict and suggest places the user might visit at a specific time based on their past travel history. In this way, the reception desk can provide a more appropriate input method by analyzing the user's past travel history. Some or all of the above processing in the reception desk may be performed using, for example, a generative AI, or not using a generative AI. For example, the reception desk can input the user's past travel history into a generative AI and obtain the optimal input method from the generative AI.

[0097] The analysis unit can improve the accuracy of its analysis by considering the user's past travel history. For example, the analysis unit can perform an optimal analysis based on the user's past travel history. The analysis unit can also perform an analysis that avoids congestion based on the user's past travel history. Furthermore, the analysis unit can analyze the user's past travel history and perform the most efficient analysis. As a result, the analysis unit improves the accuracy of its analysis by considering the user's past travel history. Some or all of the above processing in the analysis unit may be performed using, for example, a generating AI, or without a generating AI. For example, the analysis unit can input the user's past travel history into a generating AI and obtain results from the generating AI to improve the accuracy of the analysis.

[0098] The generation unit can generate appropriate tours by analyzing the user's past travel history during the generation process. For example, the generation unit can generate the optimal tour based on the user's past travel history. The generation unit can also generate tours that avoid crowds based on the user's past travel history. Furthermore, the generation unit can analyze the user's past travel history and generate the most efficient tour. In this way, the generation unit can provide more appropriate tours by analyzing the user's past travel history. Some or all of the above processing in the generation unit may be performed using, for example, a generation AI, or without a generation AI. For example, the generation unit can input the user's past travel history into a generation AI and obtain results from the generation AI to generate the optimal tour.

[0099] The display unit can select the optimal display method by referring to the user's past operation history when displaying information. For example, the display unit can provide the optimal display method based on the user's past operation history. The display unit can also provide a highly visible display method based on the user's past operation history. Furthermore, the display unit can analyze the user's past operation history and provide the most efficient display method. In this way, the display unit can provide a more appropriate display method by referring to the user's past operation history. Some or all of the above processing in the display unit may be performed using, for example, a generation AI, or without a generation AI. For example, the display unit can input data of the user's past operation history into a generation AI and obtain the optimal display method from the generation AI.

[0100] The reception desk can prioritize inputting highly relevant information by considering the user's geographical location when the user inputs their travel destination and interests. For example, the reception desk can prioritize suggesting tourist destinations close to the user's current location. The reception desk can also suggest relevant activities based on the user's geographical location. Furthermore, the reception desk can suggest the most suitable travel destination by considering the user's geographical location. In this way, the reception desk can provide more relevant information by considering the user's geographical location. Some or all of the above processing in the reception desk may be performed using, for example, a generative AI, or without a generative AI. For example, the reception desk can input the user's geographical location into a generative AI and obtain highly relevant information from the generative AI.

[0101] The following briefly describes the processing flow for example form 2.

[0102] Step 1: The reception desk inputs the user's travel destination and interests. These include tourist attractions, activities, and food preferences. The reception desk provides an interface for the user to input their travel destination and interests. Step 2: The analysis unit analyzes the group's characteristics based on the information entered by the reception unit. These characteristics include age group, number of participants, and common interests. The analysis unit collects and analyzes data such as user interests, participation frequency, and experience. Step 3: The generation unit generates the optimal sightseeing tour based on the results analyzed by the analysis unit. An appropriate sightseeing tour includes the tour content, duration, budget, etc. The generation unit collects user feedback and incorporates it into future sightseeing tours. Step 4: The display unit overlays the tour information generated by the generation unit onto the real world. The display unit overlays the tour information generated using AR technology onto the real world.

[0103] 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.

[0104] 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 the following. 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 (for example, 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. 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 a variety of operations, but is not limited to these examples. Furthermore, AI may also be an AI agent. Also, when the operations described above are performed by AI, the operations may be performed partially or entirely by AI, but is not limited to these examples. Additionally, operations performed by AI, including generative AI, may be replaced by rule-based operations, and rule-based operations may be replaced by operations performed by AI, including generative AI.

[0105] 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.

[0106] The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0107] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0112] 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).

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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.).

[0119] 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.

[0120] 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. 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.

[0121] 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.

[0122] The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0123] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0128] 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).

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.).

[0135] 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.

[0136] 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. 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.

[0137] 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.

[0138] The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0139] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0144] 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).

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.).

[0152] 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.

[0153] 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. 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.

[0154] 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.

[0155] The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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."

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] [Explanation of symbols]

[0175] 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 reception area where users input their travel destination and interests, An analysis unit analyzes the situation of the group based on the information entered by the reception unit, A generation unit generates an appropriate sightseeing tour based on the results of the analysis performed by the aforementioned analysis unit, A display unit overlays the tour information generated by the generation unit onto the real world, Equipped with A system characterized by the following features.

2. The aforementioned analysis unit, Collect and analyze data on user interests, participation frequency, and experience. The system according to feature 1.

3. The generating unit is Generate appropriate sightseeing tours based on analyzed data. The system according to feature 1.

4. The aforementioned display unit is The generated tour information is overlaid onto the real world and displayed. The system according to feature 1.

5. The generating unit is We collect user feedback and incorporate it into future tours. The system according to feature 1.

6. The aforementioned display unit is The service provides an effect where characters appear through AR. The system according to feature 1.

7. The aforementioned reception unit is It estimates the user's emotions and adjusts how they input their travel destinations and interests based on those emotions. The system according to feature 1.

8. The aforementioned reception unit is Analyze the user's past travel history and select the appropriate input method. The system according to feature 1.

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