system

Abstract

The system according to this embodiment aims to provide customers with comprehensive information about tourist destinations and optimal sightseeing routes. [Solution] The system according to the embodiment comprises a reception unit, a selection unit, a generation unit, and a proposal unit. The reception unit receives inquiries from customers regarding tourist destinations. The selection unit selects candidate tourist destinations based on the information received by the reception unit. The generation unit creates appealing videos related to the tourist destinations selected by the selection unit. The proposal unit proposes specific routes based on the videos generated by the generation unit.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance as a response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, there is a problem that the information provided regarding tourist destinations is fragmentary, making it difficult for customers to find an optimal tourist route.

[0005] The system according to the embodiment aims to provide comprehensive information regarding tourist destinations and an optimal tourist route to customers.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a reception unit, a selection unit, a generation unit, and a proposal unit. The reception unit receives inquiries from customers regarding tourist destinations. The selection unit selects candidate tourist destinations based on the information received by the reception unit. The generation unit creates appealing videos related to the tourist destinations selected by the selection unit. The proposal unit proposes specific routes based on the videos generated by the generation unit. [Effects of the Invention]

[0007] The system according to this embodiment can provide customers with comprehensive information about tourist destinations and optimal sightseeing routes. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

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

[0019] The smart device 14 comprises a computer 36, a receiving device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The receiving device 38, output device 40, and camera 42 are also connected to the bus 52.

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

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

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

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

[0024] As shown in Figure 2, in the data processing device 12, a specific processing is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" related to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

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

[0026] In the smart device 14, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used in conjunction with the specific processing program 56 by the data processing system 10. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart device 14 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

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

[0028] (Example of form 1) The tourism advisory system according to an embodiment of the present invention is a system that selects multiple tourist destination candidates for a tourist destination consulted by a customer, creates and proposes an appealing video about related people and history. In this tourism advisory system, the customer consults about a tourist destination, the AI ​​selects multiple tourist destination candidates related to that destination, the AI ​​creates an appealing video about people and history related to the selected tourist destination, and proposes it to the customer. The customer picks out places that interest them from the proposed multiple tourist destinations, and the AI ​​also proposes a specific route to visit them. For example, when a customer consults about a tourist destination, they might say, "I want you to tell me about a specific tourist destination." This information is input into the AI. Next, the AI ​​analyzes the input information and selects multiple tourist destination candidates related to that destination. For example, if it is a specific tourist destination, that specific tourist destination will be selected. The AI ​​creates an appealing video about people and history related to the selected tourist destination. For example, it creates a video about the history and people related to a specific tourist destination and proposes it to the customer. The customer picks out places that interest them from the proposed multiple tourist destinations. For example, if they are interested in a specific tourist destination, they select it. Finally, the AI ​​suggests a specific route for visiting the selected tourist spots. For example, it can suggest the optimal route from one tourist spot to another. In this way, customers can enjoy sightseeing efficiently. This allows the tourism advisory system to receive inquiries from customers about their tourist destinations, select potential tourist spots, create appealing videos, and suggest specific routes.

[0029] The tourism advisory system according to this embodiment comprises a reception unit, a selection unit, a generation unit, and a proposal unit. The reception unit receives inquiries from customers regarding tourist destinations. These inquiries include, but are not limited to, tourist spots, accommodations, and transportation. The reception unit receives, for example, information that customers have inquired about regarding tourist destinations. The selection unit selects candidate tourist destinations based on the information received by the reception unit. The selection criteria and methods for selecting candidate tourist destinations include, for example, popularity, accessibility, and seasonal appeal. The selection unit analyzes the information received by the reception unit and selects candidate tourist destinations. The generation unit creates promotional videos related to the tourist destinations selected by the selection unit. The specific content and creation methods of the promotional videos include, for example, the length of the video, the video materials used, and editing techniques. The generation unit creates, for example, promotional videos related to people and history associated with the tourist destinations selected by the selection unit. The proposal unit proposes specific routes based on the videos generated by the generation unit. The specific route suggestion criteria and methods include, but are not limited to, the means of transportation, the time required, and the order of tourist spots. For example, the suggestion unit proposes a specific route that visits tourist spots selected by the customer, based on a video generated by the generation unit. This enables the tourism advisory system according to the embodiment to receive consultations from customers regarding tourist destinations, select potential tourist spots, create appealing videos, and propose specific routes.

[0030] The reception desk can analyze a customer's past travel history and select a consultation method. For example, the reception desk can suggest new tourist destinations based on the tourist destinations the customer has visited in the past. The reception desk can also analyze a customer's preferred type of tourist destination from their past travel history and select the most suitable consultation method. Furthermore, the reception desk can prioritize suggesting consultation methods (chat, voice, etc.) that the customer has used in the past. This allows for more effective suggestions by selecting the most suitable consultation method based on the customer's past travel history. The specific content and analysis methods of past travel history include, but are not limited to, places visited, length of stay, and ratings. Some or all of the above processing in the reception desk may be performed using AI, or not. For example, the reception desk can input the customer's past travel history data into a generating AI and have the generating AI select the most suitable consultation method.

[0031] The reception desk can filter customer inquiries based on their current interests and preferences. For example, it can filter tourist destinations based on themes the customer is currently interested in (history, nature, etc.). It can also analyze the customer's recent search history and prioritize suggesting relevant tourist destinations. Furthermore, it can suggest tourist destinations of interest based on information from social media groups the customer participates in. This allows for more appropriate suggestions by filtering tourist destinations based on the customer's current interests. Specific methods for identifying current interests include, but are not limited to, recent search history and social media posts. Some or all of the above processing in the reception desk may be performed using AI, or not. For example, the reception desk can input customer search history data into a generating AI and have the AI ​​identify interests.

[0032] The reception desk can prioritize highly relevant tourist destinations when receiving inquiries, taking into account the customer's geographical location. For example, the reception desk can prioritize suggesting tourist destinations close to the customer's current location. It can also suggest easily accessible tourist destinations based on the customer's geographical location. Furthermore, it can suggest tourist destinations with convenient transportation options, taking the customer's geographical location into consideration. This allows for more appropriate suggestions by considering the customer's geographical location. Specific methods for acquiring and using geographical location information include, but are not limited to, GPS data and address information. Some or all of the above processing in the reception desk may be performed using AI, or without AI. For example, the reception desk can input the customer's geographical location data into a generating AI and have the AI ​​select highly relevant tourist destinations.

[0033] The reception desk can analyze a customer's social media activity when receiving an inquiry and then suggest relevant tourist destinations. For example, the reception desk can prioritize suggesting tourist destinations that the customer has "liked" on social media. It can also suggest relevant tourist destinations based on the tourist destination-related accounts that the customer follows. Furthermore, the reception desk can analyze the customer's social media posts and suggest tourist destinations of interest. By analyzing the customer's social media activity, it becomes possible to suggest more appropriate tourist destinations. Specific methods for analyzing and using social media activity include, but are not limited to, posts, the number of likes, and follower reactions. Some or all of the above processing in the reception desk may be performed using AI, or not. For example, the reception desk can input the customer's social media data into a generating AI and have the generating AI select relevant tourist destinations.

[0034] The selection unit can improve the accuracy of its selection process by considering the interrelationships between tourist destinations when selecting potential tourist spots. For example, the selection unit can group nearby tourist destinations together to allow for efficient travel. It can also select related tourist destinations by considering their historical connections. Furthermore, it can select related tourist destinations based on their themes (nature, history, culture, etc.). This allows for more efficient selection of tourist destinations by considering the interrelationships between them. Specific methods for considering the interrelationships between tourist destinations include, but are not limited to, geographical proximity and thematic commonality. Some or all of the above processing in the selection unit may be performed using AI, for example, or without AI. For example, the selection unit can input interrelationship data between tourist destinations into a generating AI and have the generating AI perform the task of improving selection accuracy.

[0035] The selection unit can consider the historical background of tourist destinations when selecting potential tourist destinations. For example, the selection unit may prioritize tourist destinations related to historical events. It may also prioritize tourist destinations related to historical figures. Furthermore, it may prioritize tourist destinations that include historical buildings or ruins. This allows for the selection of more appropriate tourist destinations by considering their historical background. Specific examples of the historical background of tourist destinations and methods of consideration include, but are not limited to, historical events and cultural significance. Some or all of the above processing in the selection unit may be performed using, for example, AI, or not using AI. For example, the selection unit can input historical background data of tourist destinations into a generating AI and have the generating AI perform the selection.

[0036] The selection unit can consider the geographical distribution of tourist destinations when selecting potential tourist spots. For example, the selection unit can group nearby tourist destinations together to allow for efficient travel. It can also prioritize tourist destinations that are easily accessible geographically. Furthermore, it can select a balanced mix of geographically diverse tourist destinations. By considering the geographical distribution of tourist destinations, it becomes possible to select more efficient tourist spots. Specific methods for considering the geographical distribution of tourist destinations include, but are not limited to, the density of tourist spots in each region and the ease of access. Some or all of the above processing in the selection unit may be performed using AI, for example, or without AI. For example, the selection unit can input geographical distribution data of tourist destinations into a generating AI and have the generating AI perform the selection.

[0037] The selection unit can improve the accuracy of its selection of tourist destinations by referring to relevant literature on those destinations. For example, the selection unit can refer to academic papers on tourist destinations to improve the accuracy of its selection. It can also refer to historical books on tourist destinations to improve the accuracy of its selection. It can also refer to travel guides on tourist destinations to improve the accuracy of its selection. This makes it possible to select more appropriate tourist destinations by referring to relevant literature on tourist destinations. The specific content and methods of referencing relevant literature on tourist destinations include, but are not limited to, tourist guidebooks, academic papers, and online reviews. Some or all of the above processing in the selection unit may be performed using AI, for example, or without AI. For example, the selection unit can input data on relevant literature on tourist destinations into a generating AI and have the generating AI perform the task of improving the accuracy of its selection.

[0038] The generation unit can adjust the level of detail in attractive videos based on the importance of the tourist destination. For example, for important tourist destinations, the generation unit can generate videos with detailed descriptions and footage. For less important tourist destinations, the generation unit can also generate videos with concise descriptions and footage. Furthermore, the generation unit can adjust the length of the video according to the importance of the tourist destination. This allows for the generation of more appropriate videos by adjusting the level of detail according to the importance of the tourist destination. Specific methods for evaluating the importance of a tourist destination include, but are not limited to, the number of visitors, historical significance, and customer ratings. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input tourist destination importance data into a generation AI and have the generation AI perform the adjustment of video detail.

[0039] The generation unit can apply different generation algorithms depending on the category of the tourist destination when generating attractive videos. For example, for natural scenic tourist destinations, the generation unit can apply an algorithm that emphasizes beautiful scenery. For historical tourist destinations, the generation unit can also apply an algorithm that explains the historical background in detail. Furthermore, for cultural tourist destinations, the generation unit can apply an algorithm that emphasizes cultural elements. By applying different generation algorithms depending on the category of the tourist destination, more appropriate videos can be generated. Specific classification methods for tourist destinations and the generation algorithms to be applied include, but are not limited to, natural scenic areas, historical buildings, and theme parks. Some or all of the above processing in the generation unit may be performed using, for example, AI, or without AI. For example, the generation unit can input tourist destination category data into a generation AI and have the generation AI execute the application of the generation algorithm.

[0040] The generation unit can incorporate relevant event information for tourist destinations when generating appealing videos. For example, the generation unit can enhance the appeal of videos by incorporating information about events held at tourist destinations. The generation unit can also create a sense of the seasons by incorporating seasonal event information for tourist destinations. Furthermore, the generation unit can suggest the best time to visit by incorporating information about special events at tourist destinations. In this way, more appealing videos are generated by incorporating relevant event information for tourist destinations. Specific details and methods of incorporating relevant event information include, but are not limited to, the date, time, location, and type of event. Some or all of the above processing in the generation unit may be performed using, for example, AI, or without AI. For example, the generation unit can input event information data for tourist destinations into a generation AI and have the generation AI perform the incorporation into the video.

[0041] The generation unit can incorporate relevant cultural elements of a tourist destination when generating appealing videos. For example, the generation unit can enhance the appeal of a tourist destination by incorporating its traditional culture and customs into the video. It can also attract visitors' interest by incorporating the local food culture of a tourist destination into the video. Furthermore, it can convey the cultural appeal of a tourist destination by incorporating its festivals and events into the video. In this way, more appealing videos are generated by incorporating relevant cultural elements of the tourist destination. Specific examples of cultural elements and methods of incorporation include, but are not limited to, traditional festivals, local food culture, and historical events. Some or all of the above processing in the generation unit may be performed using, for example, AI, or not using AI. For example, the generation unit can input cultural element data of a tourist destination into a generation AI and have the generation AI perform the incorporation into the video.

[0042] The suggestion unit can select a route by analyzing the customer's past travel behavior when proposing a route. For example, the suggestion unit can propose a new relevant travel route based on the tourist destinations the customer has visited in the past. The suggestion unit can also analyze the customer's preferred type of tourist destination from their past travel behavior and select the optimal route. Furthermore, the suggestion unit can propose the optimal route by considering the mode of transportation the customer has used in the past. This makes it possible to propose more effective routes by selecting the optimal route based on the customer's past travel behavior. The specific content and analysis methods of past travel behavior include, but are not limited to, places visited, length of stay, and evaluation. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input the customer's past travel behavior data into a generating AI and have the generating AI perform route selection.

[0043] The suggestion unit can customize routes based on the customer's current lifestyle when proposing routes. For example, if the customer is busy, the suggestion unit can suggest a sightseeing route that can be completed in a short time. Alternatively, if the customer wants to relax, the suggestion unit can suggest a leisurely sightseeing route. Furthermore, if the customer is traveling with family, the suggestion unit can suggest a route that includes attractions suitable for children. By customizing the route according to the customer's current lifestyle, it becomes possible to suggest more appropriate routes. Specific details of the current lifestyle and methods of customization include, but are not limited to, work schedules, family structure, and health status. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or not. For example, the suggestion unit can input customer lifestyle data into a generating AI and have the generating AI perform route customization.

[0044] The suggestion unit can select the optimal route by considering the customer's geographical location information when suggesting routes. For example, the suggestion unit can prioritize suggesting tourist spots close to the customer's current location. It can also suggest easily accessible tourist routes based on the customer's geographical location information. Furthermore, it can suggest tourist routes with convenient transportation options by considering the customer's geographical location information. By suggesting routes while considering the customer's geographical location information, more appropriate route suggestions become possible. Specific methods for acquiring and using geographical location information include, but are not limited to, GPS data and address information. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input the customer's geographical location data into a generating AI and have the generating AI select the optimal route.

[0045] The suggestion unit can analyze the customer's social media activity when suggesting routes. For example, the suggestion unit can suggest routes that include tourist spots that the customer has "liked" on social media. It can also suggest relevant tourist routes based on tourist destination-related accounts that the customer follows. Furthermore, the suggestion unit can analyze the content of the customer's social media posts and suggest routes that include tourist spots of interest. This allows for more appropriate route suggestions by analyzing the customer's social media activity. Specific methods for analyzing and using social media activity include, but are not limited to, posts, the number of likes, and follower reactions. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or not. For example, the suggestion unit can input the customer's social media data into a generating AI and have the generating AI perform route suggestions.

[0046] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0047] The suggestion unit can also propose sightseeing routes that take into account the customer's health condition. For example, if a customer is not confident in their physical fitness, it can suggest a sightseeing route that can be completed in a short time. Furthermore, if a customer leads a healthy lifestyle, the suggestion unit can suggest an active sightseeing route that includes walking or hiking. It can also suggest a sightseeing route that takes into account a customer's specific health problems. This makes it possible to suggest sightseeing routes tailored to the customer's health condition. Specific methods for considering health conditions include, but are not limited to, the customer's medical history, fitness data, and self-reported information. Some or all of the above processing in the suggestion unit may be performed using AI, or not. For example, the suggestion unit can input customer health data into a generating AI and have the generating AI generate sightseeing route suggestions.

[0048] The selection unit can also consider the customer's budget when selecting potential tourist destinations. For example, if the budget is limited, it can prioritize selecting low-cost tourist destinations. Conversely, if the budget is generous, the selection unit can select luxury tourist destinations or those offering special experiences. Furthermore, the selection unit can adjust the selection criteria for tourist destinations according to the customer's budget. This allows for the selection of tourist destinations that fit the customer's budget. Specific methods for considering the budget include, but are not limited to, the customer's spending history, self-reported budget, and credit card usage history. Some or all of the above processing in the selection unit may be performed using AI, or not. For example, the selection unit can input customer budget data into a generating AI and have the generating AI perform the selection of tourist destinations.

[0049] The generation unit can also adjust the language of an engaging video based on the customer's language settings when generating it. For example, if the customer speaks English, it can generate a video narrated in English. The generation unit can also generate a video narrated in Japanese if the customer speaks Japanese, and even generate a video narrated in multiple languages ​​if the customer speaks multiple languages. This enables the generation of engaging videos tailored to the customer's language settings. Specific methods for considering language settings include, but are not limited to, customer profile information, device language settings, and self-reporting. Some or all of the above-described processes in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input customer language setting data into a generation AI and have the generation AI perform the video language adjustment.

[0050] The reception desk can also analyze customers' past reviews and ratings to adjust the suggested tourist destinations. For example, it can suggest new tourist destinations related to those that customers have previously given high ratings to. The reception desk can also adjust the suggestions to avoid tourist destinations that customers have previously given low ratings to. Furthermore, the reception desk can analyze customers' past reviews to identify and suggest preferred types of tourist destinations. This enables the suggestion of tourist destinations based on customers' past reviews and ratings. Specific methods for analyzing reviews and ratings include, but are not limited to, customer rating scores, review content, and rating frequency. Some or all of the above processing in the reception desk may be performed using AI, or not. For example, the reception desk can input customer review and rating data into a generating AI and have the generating AI adjust the suggested tourist destinations.

[0051] The selection unit can also consider the customer's travel purpose when selecting potential tourist destinations. For example, if the customer's purpose is relaxation, it will prioritize quiet and peaceful tourist destinations. Alternatively, if the customer's purpose is adventure, it can select active tourist destinations. Furthermore, if the customer's purpose is cultural experience, it can select historical or cultural tourist destinations. This allows for the selection of tourist destinations tailored to the customer's travel purpose. Specific methods for considering travel purpose include, but are not limited to, customer self-declaration, past travel history, and travel plans. Some or all of the above processing in the selection unit may be performed using AI, or not. For example, the selection unit can input customer travel purpose data into a generating AI and have the generating AI perform the selection of tourist destinations.

[0052] The generation unit can also adjust the style of an engaging video based on the customer's visual preferences when generating it. For example, if a customer prefers vibrant colors, it can generate a video using vibrant colors. The generation unit can also generate a video using monochrome images if, for example, a customer prefers monochrome images. Furthermore, if, for example, a customer prefers animation, it can generate a video that heavily utilizes animation. This enables the generation of engaging videos tailored to the customer's visual preferences. Specific methods for considering visual preferences include, but are not limited to, customer profile information, past viewing history, and self-reported information. Some or all of the above-described processes in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input customer visual preference data into a generation AI and have the generation AI perform the video style adjustments.

[0053] The reception desk can also adjust the suggested tourist destinations based on the customer's hobbies and interests. For example, if a customer is interested in history, it will prioritize suggesting historical tourist destinations. Similarly, if a customer prefers nature, it can suggest tourist destinations with natural scenery. Furthermore, if a customer is interested in art, it can suggest tourist destinations that include art galleries and museums. This allows for the suggestion of tourist destinations tailored to the customer's hobbies and interests. Specific methods for considering hobbies and interests include, but are not limited to, customer profile information, past travel history, and self-reporting. Some or all of the above processing at the reception desk may be performed using AI, or not. For example, the reception desk can input customer hobbies and interests data into a generating AI and have the generating AI adjust the suggested tourist destinations.

[0054] The following briefly describes the processing flow for example form 1.

[0055] Step 1: The reception desk receives inquiries from customers regarding tourist destinations. These inquiries may include, but are not limited to, tourist attractions, accommodations, and transportation. Step 2: The selection department selects potential tourist destinations based on the information received by the reception department. The selection criteria and methods for potential tourist destinations include, but are not limited to, popularity, accessibility, and seasonal appeal. Step 3: The generation unit creates an appealing video related to the tourist destination selected by the selection unit. The specific content and creation method of the appealing video may include, but are not limited to, the length of the video, the video materials used, and the editing techniques. Step 4: The suggestion unit proposes a specific route based on the video generated by the generation unit. The criteria and methods for proposing a specific route include, but are not limited to, the means of transportation, the time required, and the order of sightseeing spots.

[0056] (Example of form 2) The tourism advisory system according to an embodiment of the present invention is a system that selects multiple tourist destination candidates for a tourist destination consulted by a customer, creates and proposes an appealing video about related people and history. In this tourism advisory system, the customer consults about a tourist destination, the AI ​​selects multiple tourist destination candidates related to that destination, the AI ​​creates an appealing video about people and history related to the selected tourist destination, and proposes it to the customer. The customer picks out places that interest them from the proposed multiple tourist destinations, and the AI ​​also proposes a specific route to visit them. For example, when a customer consults about a tourist destination, they might say, "I want you to tell me about a specific tourist destination." This information is input into the AI. Next, the AI ​​analyzes the input information and selects multiple tourist destination candidates related to that destination. For example, if it is a specific tourist destination, that specific tourist destination will be selected. The AI ​​creates an appealing video about people and history related to the selected tourist destination. For example, it creates a video about the history and people related to a specific tourist destination and proposes it to the customer. The customer picks out places that interest them from the proposed multiple tourist destinations. For example, if they are interested in a specific tourist destination, they select it. Finally, the AI ​​suggests a specific route for visiting the selected tourist spots. For example, it can suggest the optimal route from one tourist spot to another. In this way, customers can enjoy sightseeing efficiently. This allows the tourism advisory system to receive inquiries from customers about their tourist destinations, select potential tourist spots, create appealing videos, and suggest specific routes.

[0057] The tourism advisory system according to this embodiment comprises a reception unit, a selection unit, a generation unit, and a proposal unit. The reception unit receives inquiries from customers regarding tourist destinations. These inquiries include, but are not limited to, tourist spots, accommodations, and transportation. The reception unit receives, for example, information that customers have inquired about regarding tourist destinations. The selection unit selects candidate tourist destinations based on the information received by the reception unit. The selection criteria and methods for selecting candidate tourist destinations include, for example, popularity, accessibility, and seasonal appeal. The selection unit analyzes the information received by the reception unit and selects candidate tourist destinations. The generation unit creates promotional videos related to the tourist destinations selected by the selection unit. The specific content and creation methods of the promotional videos include, for example, the length of the video, the video materials used, and editing techniques. The generation unit creates, for example, promotional videos related to people and history associated with the tourist destinations selected by the selection unit. The proposal unit proposes specific routes based on the videos generated by the generation unit. The specific route suggestion criteria and methods include, but are not limited to, the means of transportation, the time required, and the order of tourist spots. For example, the suggestion unit proposes a specific route that visits tourist spots selected by the customer, based on a video generated by the generation unit. This enables the tourism advisory system according to the embodiment to receive consultations from customers regarding tourist destinations, select potential tourist spots, create appealing videos, and propose specific routes.

[0058] The reception desk can estimate the customer's emotions and adjust the content of the tourist information based on those emotions. For example, if the customer is excited, the reception desk can provide detailed tourist information to pique their interest. Alternatively, if the customer is relaxed, the reception desk can provide concise and easy-to-understand information to reduce stress. If the customer is feeling anxious, the reception desk can provide reassuring information to increase their trust. By adjusting the tourist information according to the customer's emotions, more appropriate information can be provided. 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 at the reception desk may be performed using AI or not. For example, the reception desk can input customer facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0059] The reception desk can analyze a customer's past travel history and select a consultation method. For example, the reception desk can suggest new tourist destinations based on the tourist destinations the customer has visited in the past. The reception desk can also analyze a customer's preferred type of tourist destination from their past travel history and select the most suitable consultation method. Furthermore, the reception desk can prioritize suggesting consultation methods (chat, voice, etc.) that the customer has used in the past. This allows for more effective suggestions by selecting the most suitable consultation method based on the customer's past travel history. The specific content and analysis methods of past travel history include, but are not limited to, places visited, length of stay, and ratings. Some or all of the above processing in the reception desk may be performed using AI, or not. For example, the reception desk can input the customer's past travel history data into a generating AI and have the generating AI select the most suitable consultation method.

[0060] The reception desk can filter customer inquiries based on their current interests and preferences. For example, it can filter tourist destinations based on themes the customer is currently interested in (history, nature, etc.). It can also analyze the customer's recent search history and prioritize suggesting relevant tourist destinations. Furthermore, it can suggest tourist destinations of interest based on information from social media groups the customer participates in. This allows for more appropriate suggestions by filtering tourist destinations based on the customer's current interests. Specific methods for identifying current interests include, but are not limited to, recent search history and social media posts. Some or all of the above processing in the reception desk may be performed using AI, or not. For example, the reception desk can input customer search history data into a generating AI and have the AI ​​identify interests.

[0061] The reception desk can estimate the customer's emotions and determine the priority of tourist destinations to recommend based on those emotions. For example, if the customer is excited, the reception desk may prioritize suggesting popular tourist destinations. Similarly, if the customer is relaxed, the reception desk may prioritize suggesting quiet and peaceful tourist destinations. Furthermore, if the customer is feeling anxious, the reception desk may prioritize suggesting safe tourist destinations. This allows for more appropriate recommendations by prioritizing tourist destinations according to the customer's emotions. Specific methods for determining the priority of tourist destinations include, but are not limited to, the customer's emotion score, popularity, and accessibility. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may include, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception desk may be performed using AI, or not. For example, the reception desk can input customer emotion data into a generative AI and have the generative AI determine the priority of tourist destinations.

[0062] The reception desk can prioritize highly relevant tourist destinations when receiving inquiries, taking into account the customer's geographical location. For example, the reception desk can prioritize suggesting tourist destinations close to the customer's current location. It can also suggest easily accessible tourist destinations based on the customer's geographical location. Furthermore, it can suggest tourist destinations with convenient transportation options, taking the customer's geographical location into consideration. This allows for more appropriate suggestions by considering the customer's geographical location. Specific methods for acquiring and using geographical location information include, but are not limited to, GPS data and address information. Some or all of the above processing in the reception desk may be performed using AI, or without AI. For example, the reception desk can input the customer's geographical location data into a generating AI and have the AI ​​select highly relevant tourist destinations.

[0063] The reception desk can analyze a customer's social media activity when receiving an inquiry and then suggest relevant tourist destinations. For example, the reception desk can prioritize suggesting tourist destinations that the customer has "liked" on social media. It can also suggest relevant tourist destinations based on the tourist destination-related accounts that the customer follows. Furthermore, the reception desk can analyze the customer's social media posts and suggest tourist destinations of interest. By analyzing the customer's social media activity, it becomes possible to suggest more appropriate tourist destinations. Specific methods for analyzing and using social media activity include, but are not limited to, posts, the number of likes, and follower reactions. Some or all of the above processing in the reception desk may be performed using AI, or not. For example, the reception desk can input the customer's social media data into a generating AI and have the generating AI select relevant tourist destinations.

[0064] The selection unit can estimate the customer's emotions and adjust the selection criteria for tourist destinations based on the estimated emotions. For example, if the customer is excited, the selection unit may prioritize active tourist destinations. Alternatively, if the customer is relaxed, the selection unit may prioritize quiet and calm tourist destinations. Furthermore, if the customer is feeling anxious, the selection unit may prioritize safe tourist destinations. By adjusting the selection criteria for tourist destinations according to the customer's emotions, it becomes possible to suggest more appropriate tourist destinations. Specific methods for adjusting the selection criteria for tourist destinations include, but are not limited to, customer emotion scores, past ratings, and current trends. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the selection unit may be performed using AI, or not using AI. For example, the selection unit can input customer sentiment data into a generating AI and have the AI ​​adjust the selection criteria for potential tourist destinations.

[0065] The selection unit can improve the accuracy of its selection process by considering the interrelationships between tourist destinations when selecting potential tourist spots. For example, the selection unit can group nearby tourist destinations together to allow for efficient travel. It can also select related tourist destinations by considering their historical connections. Furthermore, it can select related tourist destinations based on their themes (nature, history, culture, etc.). This allows for more efficient selection of tourist destinations by considering the interrelationships between them. Specific methods for considering the interrelationships between tourist destinations include, but are not limited to, geographical proximity and thematic commonality. Some or all of the above processing in the selection unit may be performed using AI, for example, or without AI. For example, the selection unit can input interrelationship data between tourist destinations into a generating AI and have the generating AI perform the task of improving selection accuracy.

[0066] The selection unit can consider the historical background of tourist destinations when selecting potential tourist destinations. For example, the selection unit may prioritize tourist destinations related to historical events. It may also prioritize tourist destinations related to historical figures. Furthermore, it may prioritize tourist destinations that include historical buildings or ruins. This allows for the selection of more appropriate tourist destinations by considering their historical background. Specific examples of the historical background of tourist destinations and methods of consideration include, but are not limited to, historical events and cultural significance. Some or all of the above processing in the selection unit may be performed using, for example, AI, or not using AI. For example, the selection unit can input historical background data of tourist destinations into a generating AI and have the generating AI perform the selection.

[0067] The selection unit can estimate the customer's emotions and adjust the display order of selected tourist spots based on the estimated emotions. For example, if the customer is excited, the selection unit can display the most popular tourist spots first. Alternatively, if the customer is relaxed, the selection unit can display quiet and calming tourist spots first. Furthermore, if the customer is feeling anxious, the selection unit can display safe tourist spots first. By adjusting the display order of tourist spots according to the customer's emotions, more appropriate suggestions can be made. Specific methods for adjusting the display order of tourist spots include, but are not limited to, customer emotion scores, popularity, and accessibility. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the selection unit may be performed using AI, or not. For example, the selection unit can input customer emotion data into a generative AI and have the generative AI adjust the display order of tourist spots.

[0068] The selection unit can consider the geographical distribution of tourist destinations when selecting potential tourist spots. For example, the selection unit can group nearby tourist destinations together to allow for efficient travel. It can also prioritize tourist destinations that are easily accessible geographically. Furthermore, it can select a balanced mix of geographically diverse tourist destinations. By considering the geographical distribution of tourist destinations, it becomes possible to select more efficient tourist spots. Specific methods for considering the geographical distribution of tourist destinations include, but are not limited to, the density of tourist spots in each region and the ease of access. Some or all of the above processing in the selection unit may be performed using AI, for example, or without AI. For example, the selection unit can input geographical distribution data of tourist destinations into a generating AI and have the generating AI perform the selection.

[0069] The selection unit can improve the accuracy of its selection of tourist destinations by referring to relevant literature on those destinations. For example, the selection unit can refer to academic papers on tourist destinations to improve the accuracy of its selection. It can also refer to historical books on tourist destinations to improve the accuracy of its selection. It can also refer to travel guides on tourist destinations to improve the accuracy of its selection. This makes it possible to select more appropriate tourist destinations by referring to relevant literature on tourist destinations. The specific content and methods of referencing relevant literature on tourist destinations include, but are not limited to, tourist guidebooks, academic papers, and online reviews. Some or all of the above processing in the selection unit may be performed using AI, for example, or without AI. For example, the selection unit can input data on relevant literature on tourist destinations into a generating AI and have the generating AI perform the task of improving the accuracy of its selection.

[0070] The generation unit can estimate the customer's emotions and adjust the presentation of the engaging video based on the estimated emotions. For example, if the customer is excited, the generation unit can generate a video with visually stimulating effects. Alternatively, if the customer is relaxed, the generation unit can generate a video using calming music and gentle visuals. Furthermore, if the customer is feeling anxious, the generation unit can generate a video using reassuring narration and visuals. By adjusting the presentation of the engaging video according to the customer's emotions, a more effective video can be generated. Specific methods for adjusting the presentation of the engaging video include, but are not limited to, the style of the visuals, the selection of music, and the presence or absence of narration. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above processing in the generation unit may be performed using AI, or not using AI. For example, the generation unit can input customer emotion data into the generation AI and have the generation AI adjust the way the appealing video is presented.

[0071] The generation unit can adjust the level of detail in attractive videos based on the importance of the tourist destination. For example, for important tourist destinations, the generation unit can generate videos with detailed descriptions and footage. For less important tourist destinations, the generation unit can also generate videos with concise descriptions and footage. Furthermore, the generation unit can adjust the length of the video according to the importance of the tourist destination. This allows for the generation of more appropriate videos by adjusting the level of detail according to the importance of the tourist destination. Specific methods for evaluating the importance of a tourist destination include, but are not limited to, the number of visitors, historical significance, and customer ratings. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input tourist destination importance data into a generation AI and have the generation AI perform the adjustment of video detail.

[0072] The generation unit can apply different generation algorithms depending on the category of the tourist destination when generating attractive videos. For example, for natural scenic tourist destinations, the generation unit can apply an algorithm that emphasizes beautiful scenery. For historical tourist destinations, the generation unit can also apply an algorithm that explains the historical background in detail. Furthermore, for cultural tourist destinations, the generation unit can apply an algorithm that emphasizes cultural elements. By applying different generation algorithms depending on the category of the tourist destination, more appropriate videos can be generated. Specific classification methods for tourist destinations and the generation algorithms to be applied include, but are not limited to, natural scenic areas, historical buildings, and theme parks. Some or all of the above processing in the generation unit may be performed using, for example, AI, or without AI. For example, the generation unit can input tourist destination category data into a generation AI and have the generation AI execute the application of the generation algorithm.

[0073] The generation unit can estimate the customer's emotions and adjust the length of the engaging video based on the estimated emotions. For example, if the customer is in a hurry, the generation unit will generate a short, concise video. Alternatively, if the customer is relaxed, the generation unit can generate a longer video with detailed explanations. Furthermore, if the customer is excited, the generation unit can generate a video with visually stimulating effects. By adjusting the length of the engaging video according to the customer's emotions, a more appropriate video can be generated. Specific methods for adjusting the length of the engaging video include, but are not limited to, the customer's emotion score, the video content, and the viewer's level of interest. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above processing in the generation unit may be performed using AI, or not. For example, the generation unit can input customer emotion data into the generative AI and have the generative AI adjust the length of the engaging video.

[0074] The generation unit can incorporate relevant event information for tourist destinations when generating appealing videos. For example, the generation unit can enhance the appeal of videos by incorporating information about events held at tourist destinations. The generation unit can also create a sense of the seasons by incorporating seasonal event information for tourist destinations. Furthermore, the generation unit can suggest the best time to visit by incorporating information about special events at tourist destinations. In this way, more appealing videos are generated by incorporating relevant event information for tourist destinations. Specific details and methods of incorporating relevant event information include, but are not limited to, the date, time, location, and type of event. Some or all of the above processing in the generation unit may be performed using, for example, AI, or without AI. For example, the generation unit can input event information data for tourist destinations into a generation AI and have the generation AI perform the incorporation into the video.

[0075] The generation unit can incorporate relevant cultural elements of a tourist destination when generating appealing videos. For example, the generation unit can enhance the appeal of a tourist destination by incorporating its traditional culture and customs into the video. It can also attract visitors' interest by incorporating the local food culture of a tourist destination into the video. Furthermore, it can convey the cultural appeal of a tourist destination by incorporating its festivals and events into the video. In this way, more appealing videos are generated by incorporating relevant cultural elements of the tourist destination. Specific examples of cultural elements and methods of incorporation include, but are not limited to, traditional festivals, local food culture, and historical events. Some or all of the above processing in the generation unit may be performed using, for example, AI, or not using AI. For example, the generation unit can input cultural element data of a tourist destination into a generation AI and have the generation AI perform the incorporation into the video.

[0076] The suggestion unit can estimate the customer's emotions and adjust the route suggestion method based on the estimated emotions. For example, if the customer is excited, the suggestion unit may suggest an active sightseeing route. Alternatively, if the customer is relaxed, it may suggest a quiet and calm sightseeing route. Furthermore, if the customer is feeling anxious, it may suggest a safe sightseeing route. By adjusting the route suggestion method according to the customer's emotions, more appropriate route suggestions become possible. Specific methods for adjusting the route suggestion method include, but are not limited to, the customer's emotion score, mode of transportation, and travel time. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the suggestion unit may be performed using AI or not. For example, the suggestion unit can input customer emotion data into a generative AI and have the generative AI adjust the route suggestion method.

[0077] The suggestion unit can select a route by analyzing the customer's past travel behavior when proposing a route. For example, the suggestion unit can propose a new relevant travel route based on the tourist destinations the customer has visited in the past. The suggestion unit can also analyze the customer's preferred type of tourist destination from their past travel behavior and select the optimal route. Furthermore, the suggestion unit can propose the optimal route by considering the mode of transportation the customer has used in the past. This makes it possible to propose more effective routes by selecting the optimal route based on the customer's past travel behavior. The specific content and analysis methods of past travel behavior include, but are not limited to, places visited, length of stay, and evaluation. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input the customer's past travel behavior data into a generating AI and have the generating AI perform route selection.

[0078] The suggestion unit can customize routes based on the customer's current lifestyle when proposing routes. For example, if the customer is busy, the suggestion unit can suggest a sightseeing route that can be completed in a short time. Alternatively, if the customer wants to relax, the suggestion unit can suggest a leisurely sightseeing route. Furthermore, if the customer is traveling with family, the suggestion unit can suggest a route that includes attractions suitable for children. By customizing the route according to the customer's current lifestyle, it becomes possible to suggest more appropriate routes. Specific details of the current lifestyle and methods of customization include, but are not limited to, work schedules, family structure, and health status. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or not. For example, the suggestion unit can input customer lifestyle data into a generating AI and have the generating AI perform route customization.

[0079] The suggestion unit can estimate the customer's emotions and determine the priority of route suggestions based on those emotions. For example, if the customer is excited, the suggestion unit will prioritize suggesting popular tourist spots. Similarly, if the customer is relaxed, the suggestion unit may prioritize suggesting quiet and peaceful tourist spots. Furthermore, if the customer is feeling anxious, the suggestion unit may prioritize suggesting safe tourist spots. This allows for more appropriate route suggestions by prioritizing route suggestions according to the customer's emotions. Specific methods for determining route suggestion priorities include, but are not limited to, customer emotion scores, popularity, and accessibility. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may include, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the suggestion unit may be performed using AI or not. For example, the suggestion unit can input customer emotion data into a generative AI and have the generative AI determine the priority of route suggestions.

[0080] The suggestion unit can select the optimal route by considering the customer's geographical location information when suggesting routes. For example, the suggestion unit can prioritize suggesting tourist spots close to the customer's current location. It can also suggest easily accessible tourist routes based on the customer's geographical location information. Furthermore, it can suggest tourist routes with convenient transportation options by considering the customer's geographical location information. By suggesting routes while considering the customer's geographical location information, more appropriate route suggestions become possible. Specific methods for acquiring and using geographical location information include, but are not limited to, GPS data and address information. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input the customer's geographical location data into a generating AI and have the generating AI select the optimal route.

[0081] The suggestion unit can analyze the customer's social media activity when suggesting routes. For example, the suggestion unit can suggest routes that include tourist spots that the customer has "liked" on social media. It can also suggest relevant tourist routes based on tourist destination-related accounts that the customer follows. Furthermore, the suggestion unit can analyze the content of the customer's social media posts and suggest routes that include tourist spots of interest. This allows for more appropriate route suggestions by analyzing the customer's social media activity. Specific methods for analyzing and using social media activity include, but are not limited to, posts, the number of likes, and follower reactions. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or not. For example, the suggestion unit can input the customer's social media data into a generating AI and have the generating AI perform route suggestions. === Hard Collateral 1-1 === Each of the multiple elements described above, including the reception unit, selection unit, generation unit, and proposal unit, is implemented, for example, by 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 receives inquiries from customers regarding tourist destinations. The selection unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12 and selects candidate tourist destinations based on the information received by the reception unit. The generation unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12 and creates attractive videos related to the tourist destinations selected by the selection unit. The proposal unit is implemented, for example, by the control unit 46A of the smart device 14 and proposes a specific route based on the videos generated by the generation unit. === Hard Collateral 1-2 === Each of the multiple elements described above, including the reception unit, selection unit, generation unit, and proposal 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 receives inquiries from customers regarding tourist destinations. The selection unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12 and selects candidate tourist destinations based on the information received by the reception unit. The generation unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12 and creates attractive videos related to the tourist destinations selected by the selection unit. The proposal unit is implemented, for example, by the control unit 46A of the smart glasses 214 and proposes a specific route based on the videos generated by the generation unit. === Hard Collateral 1-3 === Each of the multiple elements described above, including the reception unit, selection unit, generation unit, and proposal unit, is implemented by, for example, 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 receives inquiries from customers regarding tourist destinations. The selection unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and selects candidate tourist destinations based on the information received by the reception unit. The generation unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and creates attractive videos related to the tourist destinations selected by the selection unit. The proposal unit is implemented by, for example, the control unit 46A of the headset terminal 314 and proposes a specific route based on the videos generated by the generation unit. === Hard Collateral 1-4 === Each of the multiple elements described above, including the reception unit, selection unit, generation unit, and proposal unit, is implemented by, for example, 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 receives inquiries from customers regarding tourist destinations. The selection unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and selects candidate tourist destinations based on the information received by the reception unit. The generation unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and creates appealing videos related to the tourist destinations selected by the selection unit. The proposal unit is implemented by, for example, the control unit 46A of the robot 414 and proposes a specific route based on the videos generated by the generation unit.

[0082] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0083] The suggestion unit can also propose sightseeing routes that take into account the customer's health condition. For example, if a customer is not confident in their physical fitness, it can suggest a sightseeing route that can be completed in a short time. Furthermore, if a customer leads a healthy lifestyle, the suggestion unit can suggest an active sightseeing route that includes walking or hiking. It can also suggest a sightseeing route that takes into account a customer's specific health problems. This makes it possible to suggest sightseeing routes tailored to the customer's health condition. Specific methods for considering health conditions include, but are not limited to, the customer's medical history, fitness data, and self-reported information. Some or all of the above processing in the suggestion unit may be performed using AI, or not. For example, the suggestion unit can input customer health data into a generating AI and have the generating AI generate sightseeing route suggestions.

[0084] The reception desk can estimate the customer's emotions and personalize the suggested tourist destinations based on those emotions. For example, if the customer is excited, it can suggest tourist destinations that include active activities. Alternatively, if the customer is relaxed, it can suggest quiet and calming tourist destinations. Furthermore, if the customer is feeling anxious, it can suggest tourist destinations that provide a sense of security. This enables the suggestion of tourist destinations that align with the customer's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may include, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can input customer emotion data into a generative AI and have the generative AI personalize the suggested tourist destinations.

[0085] The selection unit can also consider the customer's budget when selecting potential tourist destinations. For example, if the budget is limited, it can prioritize selecting low-cost tourist destinations. Conversely, if the budget is generous, the selection unit can select luxury tourist destinations or those offering special experiences. Furthermore, the selection unit can adjust the selection criteria for tourist destinations according to the customer's budget. This allows for the selection of tourist destinations that fit the customer's budget. Specific methods for considering the budget include, but are not limited to, the customer's spending history, self-reported budget, and credit card usage history. Some or all of the above processing in the selection unit may be performed using AI, or not. For example, the selection unit can input customer budget data into a generating AI and have the generating AI perform the selection of tourist destinations.

[0086] The generation unit can also adjust the language of an engaging video based on the customer's language settings when generating it. For example, if the customer speaks English, it can generate a video narrated in English. The generation unit can also generate a video narrated in Japanese if the customer speaks Japanese, and even generate a video narrated in multiple languages ​​if the customer speaks multiple languages. This enables the generation of engaging videos tailored to the customer's language settings. Specific methods for considering language settings include, but are not limited to, customer profile information, device language settings, and self-reporting. Some or all of the above-described processes in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input customer language setting data into a generation AI and have the generation AI perform the video language adjustment.

[0087] The suggestion unit can estimate the customer's emotions and adjust the duration of the sightseeing route based on those emotions. For example, if the customer is excited, it can suggest a route that visits many tourist spots in a short amount of time. Alternatively, if the customer is relaxed, it can suggest a route that visits tourist spots at a leisurely pace. Furthermore, if the customer is feeling anxious, it can suggest a shorter route that allows for a more relaxed experience. This enables the adjustment of the sightseeing route duration according to the customer's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may include, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the suggestion unit may be performed using AI or not. For example, the suggestion unit can input customer emotion data into a generative AI and have the generative AI adjust the duration of the sightseeing route.

[0088] The reception desk can also analyze customers' past reviews and ratings to adjust the suggested tourist destinations. For example, it can suggest new tourist destinations related to those that customers have previously given high ratings to. The reception desk can also adjust the suggestions to avoid tourist destinations that customers have previously given low ratings to. Furthermore, the reception desk can analyze customers' past reviews to identify and suggest preferred types of tourist destinations. This enables the suggestion of tourist destinations based on customers' past reviews and ratings. Specific methods for analyzing reviews and ratings include, but are not limited to, customer rating scores, review content, and rating frequency. Some or all of the above processing in the reception desk may be performed using AI, or not. For example, the reception desk can input customer review and rating data into a generating AI and have the generating AI adjust the suggested tourist destinations.

[0089] The selection unit can also consider the customer's travel purpose when selecting potential tourist destinations. For example, if the customer's purpose is relaxation, it will prioritize quiet and peaceful tourist destinations. Alternatively, if the customer's purpose is adventure, it can select active tourist destinations. Furthermore, if the customer's purpose is cultural experience, it can select historical or cultural tourist destinations. This allows for the selection of tourist destinations tailored to the customer's travel purpose. Specific methods for considering travel purpose include, but are not limited to, customer self-declaration, past travel history, and travel plans. Some or all of the above processing in the selection unit may be performed using AI, or not. For example, the selection unit can input customer travel purpose data into a generating AI and have the generating AI perform the selection of tourist destinations.

[0090] The generation unit can also adjust the style of an engaging video based on the customer's visual preferences when generating it. For example, if a customer prefers vibrant colors, it can generate a video using vibrant colors. The generation unit can also generate a video using monochrome images if, for example, a customer prefers monochrome images. Furthermore, if, for example, a customer prefers animation, it can generate a video that heavily utilizes animation. This enables the generation of engaging videos tailored to the customer's visual preferences. Specific methods for considering visual preferences include, but are not limited to, customer profile information, past viewing history, and self-reported information. Some or all of the above-described processes in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input customer visual preference data into a generation AI and have the generation AI perform the video style adjustments.

[0091] The suggestion unit can estimate the customer's emotions and adjust the mode of transportation on the sightseeing route based on those emotions. For example, if the customer is excited, it can suggest active modes of transportation (bicycle, walking, etc.). Alternatively, if the customer is relaxed, it can suggest comfortable modes of transportation (car, bus, etc.). Furthermore, if the customer is feeling anxious, it can suggest safer modes of transportation (taxi, private car, etc.). This allows for adjustment of the sightseeing route's mode of transportation according to the customer's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the suggestion unit may be performed using AI or not. For example, the suggestion unit can input customer emotion data into a generative AI and have the generative AI adjust the sightseeing route's mode of transportation.

[0092] The reception desk can also adjust the suggested tourist destinations based on the customer's hobbies and interests. For example, if a customer is interested in history, it will prioritize suggesting historical tourist destinations. Similarly, if a customer prefers nature, it can suggest tourist destinations with natural scenery. Furthermore, if a customer is interested in art, it can suggest tourist destinations that include art galleries and museums. This allows for the suggestion of tourist destinations tailored to the customer's hobbies and interests. Specific methods for considering hobbies and interests include, but are not limited to, customer profile information, past travel history, and self-reporting. Some or all of the above processing at the reception desk may be performed using AI, or not. For example, the reception desk can input customer hobbies and interests data into a generating AI and have the generating AI adjust the suggested tourist destinations.

[0093] The following briefly describes the processing flow for example form 2.

[0094] Step 1: The reception desk receives inquiries from customers regarding tourist destinations. These inquiries may include, but are not limited to, tourist attractions, accommodations, and transportation. Step 2: The selection department selects potential tourist destinations based on the information received by the reception department. The selection criteria and methods for potential tourist destinations include, but are not limited to, popularity, accessibility, and seasonal appeal. Step 3: The generation unit creates an appealing video related to the tourist destination selected by the selection unit. The specific content and creation method of the appealing video may include, but are not limited to, the length of the video, the video materials used, and the editing techniques. Step 4: The suggestion unit proposes a specific route based on the video generated by the generation unit. The criteria and methods for proposing a specific route include, but are not limited to, the means of transportation, the time required, and the order of sightseeing spots.

[0095] 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.

[0096] 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.

[0097] 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.

[0098] The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0099] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0100] 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.

[0101] 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.

[0102] 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.

[0103] 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.

[0104] 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).

[0105] 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.

[0106] 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.

[0107] 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.

[0108] 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.

[0109] 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.

[0110] 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.).

[0111] 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.

[0112] 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.

[0113] 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.

[0114] The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0115] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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).

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.).

[0127] 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.

[0128] 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.

[0129] 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.

[0130] The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0131] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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).

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.).

[0144] 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.

[0145] 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.

[0146] 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.

[0147] The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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."

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] [Explanation of symbols]

[0167] 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 desk that handles customer inquiries about tourist destinations, A selection department selects potential tourist destinations based on the information received by the aforementioned reception department, A generation unit that creates an appealing video related to the tourist destination selected by the selection unit, A proposal unit proposes a specific route based on the video generated by the generation unit, Equipped with A system characterized by the following features.

2. The aforementioned reception unit is We estimate customer emotions and adjust the content of tourist destination consultations based on those estimated emotions. The system according to feature 1.

3. The aforementioned reception unit is We analyze the customer's past travel history and select the appropriate consultation method. The system according to feature 1.

4. The aforementioned reception unit is When receiving inquiries, filtering is performed based on the customer's current interests and concerns. The system according to feature 1.

5. The aforementioned reception unit is The system estimates customer emotions and prioritizes tourist destinations based on those estimated emotions. The system according to feature 1.

6. The aforementioned reception unit is When receiving inquiries, the system prioritizes recommending highly relevant tourist destinations based on the customer's geographical location. The system according to feature 1.

7. The aforementioned reception unit is When receiving inquiries, the system analyzes the customer's social media activity and identifies relevant tourist destinations. The system according to feature 1.

8. The aforementioned selection unit is, We estimate customer sentiment and adjust the selection criteria for potential tourist destinations based on that estimated sentiment. The system according to feature 1.

Images