System

Abstract

An object of a system according to an embodiment is to provide a personalized Guided Tour according to needs and interests of a tourist.SOLUTION: A system according to an embodiment includes a generation AI, a guide learning unit, a personalization provision unit, and a double guide implementation unit. The guide learner learns the guide's gesture, way of speaking, facial expression, and way of development of speech. The personalization provision unit provides personalized Guided Tour according to the needs and interests of the tourist on the basis of the information learned by the guide learning unit. The double guide implementation unit guides the tourist in real time based on the Guided Tour provided by the personalization provision unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

[0002] Patent document 1 discloses a persona chatbot control method performed by at least one processor, the method including the steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to a description of the chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Japanese Patent Publication No. 2022-180282 Summary of the Invention [Problem to be solved by the invention]

[0004] Conventional technology has had the problem of making it difficult to provide personalized guided tours that meet the needs and interests of tourists.

[0005] The system according to the embodiment aims to provide personalized guided tours that meet the needs and interests of tourists. [Means for solving the problem]

[0006] The system according to the embodiment comprises a generation AI, a guide learning unit, a personalization providing unit, and an avatar guide implementation unit. The guide learning unit learns the guide's gestures, speaking style, facial expressions, and story development. The personalization providing unit provides a personalized guided tour tailored to the tourist's needs and interests based on the information learned by the guide learning unit. The avatar guide implementation unit guides tourists in real time based on the guided tour provided by the personalization providing unit. [Effects of the Invention]

[0007] The system according to the embodiment can provide personalized guided tours according to the needs and interests of tourists. [Brief explanation of the drawings]

[0008] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10]1 shows an emotion map onto which multiple emotions are mapped. DETAILED DESCRIPTION OF THE INVENTION

[0009] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0010] First, the terms used in the following description will be explained.

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

[0012] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

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

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

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

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

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

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

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

[0020] The reception device 38 includes a touch panel 38A and a microphone 38B, and receives user input. The touch panel 38A detects contact with a pointer (for example, a pen or a finger) to receive user input by the touch of the pointer. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 (see FIG. 2) acquires the data indicating the user input.

[0021] Output device 40 includes a display 40A and a speaker 40B, and presents data to a user by outputting the data in a form of expression that the user can perceive (e.g., audio and / or text). Display 40A displays visible information such as text and images in accordance with instructions from processor 46. Speaker 40B outputs audio in accordance with instructions from processor 46. Camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

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

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

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

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

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

[0028] (Example 1) The guided tour providing system according to an embodiment of the present invention is a system that uses generation AI to provide personalized guided tours to tourists visiting Japan. As a result, the guided tour providing system provides individually customized guided tours to tourists visiting Japan, and the guides themselves can receive compensation.

[0029] A guided tour providing system according to an embodiment includes a generation AI, a guide learning unit, a personalization providing unit, and an avatar guide implementation unit. The generation AI includes a guide learning unit that learns a guide's gestures, speaking style, facial expressions, and story development. For example, the guide learning unit analyzes and learns a guide's movements and speaking style using video and audio data of the guide guiding around tourist attractions. The guide learning unit can also learn a guide's gestures and speaking style using video analysis, audio analysis, and facial expression recognition technology. The guide learning unit can also learn a guide's emotions and psychological state to provide a more human-like guide. The personalization providing unit provides a personalized guided tour based on the information learned by the guide learning unit, tailored to the needs and interests of tourists. For example, the personalization providing unit uses prompts containing the tourist's interests and needs to provide detailed information about tourist attractions using the generation AI. The personalization providing unit can also analyze a tourist's past travel history and social media posts to achieve more accurate personalization. The personalization providing unit can also collect real-time behavioral data from tourists and provide the optimal guided tour on the spot. The avatar guide implementation unit provides real-time guidance to tourists based on the guided tour provided by the personalization provision unit. For example, the avatar guide implementation unit allows tourists to tour tourist attractions while interacting with the avatar guide via a smartphone or tablet. The avatar guide implementation unit can also analyze tourists' real-time reactions and respond immediately. The avatar guide implementation unit can also use an emotion estimation function to adjust the guide's facial expression and tone in real time according to the tourist's emotions. This allows the guided tour provision system according to the embodiment to provide individually customized guided tours to tourists visiting Japan, and the guide himself / herself can receive compensation. For example, the guide's compensation can be varied based on the tourist's satisfaction and feedback. The guide's compensation can also be increased or decreased based on the frequency of use and popularity of the avatar guide. The guide's compensation can also be increased based on sales of products and services purchased by tourists through the avatar guide.

[0030] The guide learning unit can add real-time reaction data of tourists and improve the adaptability of the guide. For example, the guide learning unit uses a smartphone or wearable device to analyze the facial expressions and voices of tourists to collect real-time reaction data of tourists. For example, it captures the moment when a tourist smiles or shows interest. The guide learning unit also uses cameras and microphones installed at tourist spots to record the behavior and reactions of tourists. For example, it analyzes how tourists react to particular exhibits. The guide learning unit also collects feedback from tourists in real time and improves the adaptability of the guide. For example, it records the impressions and opinions that tourists have during the tour in real time and uses them as learning data. In this way, the adaptability of the guide can be improved by adding real-time reaction data of tourists.

[0031] The personalization providing unit can analyze a tourist's past travel history or the content of their posts on social media to achieve more accurate personalization. The personalization providing unit, for example, analyzes a tourist's past travel history and has the generation AI learn from it. For example, a personalized guided tour is provided based on tourist attractions that the tourist has visited in the past and activities that the tourist has been interested in. The personalization providing unit also analyzes the content of tourist's posts on social media and has the generation AI learn from it. For example, it analyzes photos and comments posted by tourists on social media to identify tourist attractions and activities that the tourist is interested in. The personalization providing unit also integrates the tourist's past travel history and the content of their posts on social media and has the generation AI learn from it. For example, it achieves more accurate personalization based on tourist attractions that the tourist has visited in the past and the content that the tourist has posted on social media. In this way, more accurate personalization can be achieved by analyzing a tourist's past travel history and the content of their posts on social media.

[0032] The personalization provision unit collects real-time behavioral data of tourists and can provide the most suitable guided tour on the spot. For example, the personalization provision unit uses a smartphone or wearable device to collect real-time behavioral data of tourists and analyzes their location information and movement patterns. For example, the personalization provision unit provides the most suitable guided tour based on the tourist's current location. The personalization provision unit also uses sensors and cameras installed at tourist spots to record the tourist's behavior. For example, if a tourist shows interest in a particular exhibit, the personalization provision unit provides detailed information about that exhibit. The personalization provision unit also collects feedback from tourists in real time and provides the most suitable guided tour on the spot. For example, the personalization provision unit records the impressions and opinions that tourists have during the tour in real time and adjusts the guided tour based on that. In this way, by collecting real-time behavioral data of tourists, the most suitable guided tour can be provided on the spot.

[0033] The avatar guide implementation unit can analyze tourists' real-time reactions and respond immediately. To analyze tourists' real-time reactions, the avatar guide implementation unit uses the camera and microphone of a smartphone or tablet to analyze their facial expressions and tone of voice. For example, it captures the moment a tourist smiles or shows interest. The avatar guide implementation unit also uses sensors and cameras installed at tourist spots to record tourists' behavior. For example, if a tourist shows interest in a particular exhibit, it provides detailed information about that exhibit. The avatar guide implementation unit also collects tourists' feedback in real time, allowing the avatar guide to respond immediately. For example, it records tourists' impressions and opinions during the tour in real time and adjusts the content of the guide based on that information. This allows the avatar guide implementation unit to analyze tourists' real-time reactions and respond immediately, thereby providing a more satisfying guide experience.

[0034] The avatar guide implementation unit can combine AR technology to visually explain the history and culture of a tourist destination. For example, the avatar guide implementation unit uses AR technology to visually explain the history and culture of a tourist destination by using the camera of a smartphone or tablet to overlay digital information on real-world scenery. For example, it displays restored images of historical buildings. The avatar guide implementation unit also uses AR technology to develop a dedicated AR app to visually explain the history and culture of a tourist destination, allowing tourists to view the information through the app. For example, AR markers are placed at important points in a tourist destination, and information is displayed when the camera is pointed at them. The avatar guide implementation unit also uses AR technology to visually explain the history and culture of a tourist destination by using models and dioramas of the tourist destination and displaying detailed information using AR. For example, when the camera is pointed at a model, the internal structure and historical background of a building are displayed. In this way, the history and culture of a tourist destination can be visually explained by combining AR technology.

[0035] The avatar guide implementation unit combines voice recognition technology to provide instant answers to tourists' questions. The avatar guide implementation unit uses, for example, voice recognition technology to analyze tourists' voices using the microphone on a smartphone or tablet to provide instant answers to their questions. For example, the questions asked by tourists are converted into text in real time, and the generation AI generates answers. The avatar guide implementation unit also uses voice recognition technology to develop a dedicated voice recognition app to provide instant answers to tourists' questions, allowing tourists to ask questions through the app. For example, when a tourist speaks to the app, the generation AI immediately displays the answer. The avatar guide implementation unit also uses voice recognition technology to analyze tourists' questions using voice recognition devices installed at tourist spots to provide instant answers to their questions. For example, a voice recognition device is installed at an information center at a tourist spot, and when a tourist asks a question, the generation AI provides an answer. In this way, by combining voice recognition technology, tourists' questions can be answered instantly.

[0036] The system can vary the compensation paid to the guide based on the tourist's satisfaction and feedback. For example, the system collects tourist satisfaction and feedback and varies the compensation paid to the guide. For example, the system determines the compensation based on surveys and reviews filled out by tourists after the tour ends. The system also collects tourist satisfaction and feedback in real time and varies the compensation paid to the guide. For example, the system records the impressions and opinions of tourists during the tour in real time and adjusts the compensation based on this. The system also analyzes tourist satisfaction and feedback and varies the compensation paid to the guide. For example, the system analyzes photos and videos taken by tourists during the tour and evaluates the quality of the guide. This makes it possible to vary the compensation paid to the guide based on the tourist's satisfaction and feedback.

[0037] The system can increase or decrease the reward paid to the guide based on the frequency of use of the avatar guide and its popularity. For example, the system collects information on how often the avatar guide is used and increases or decreases the reward paid to the guide. For example, each time a tourist uses the avatar guide, the system records the number of times the avatar guide is used and determines the reward. The system also collects information on the popularity of the avatar guide and increases or decreases the reward paid to the guide. For example, if a tourist gives the avatar guide a high rating, the system adjusts the reward based on that rating. The system also analyzes the frequency of use and popularity of the avatar guide and increases or decreases the reward paid to the guide. For example, the system determines the reward based on the number of times a tourist uses the avatar guide and their rating. In this way, the system can increase or decrease the reward paid to the guide based on the frequency of use and popularity of the avatar guide.

[0038] The system can increase a guide's reward based on collaboration with other guides or the implementation of a joint tour. For example, the system increases the reward based on collaboration with other guides or the implementation of a joint tour. For example, if multiple guides jointly implement a tour, the reward is shared. The system also increases the reward based on collaboration with other guides or the implementation of a joint tour. For example, if a guide cooperates with other guides to implement a special event or tour, the reward is increased. The system also increases the reward based on collaboration with other guides or the implementation of a joint tour. For example, if a guide develops a new tour plan in collaboration with other guides, the reward is increased. In this way, a guide's reward can be increased based on collaboration with other guides or the implementation of a joint tour.

[0039] The system can increase the remuneration to the guide based on the sales of goods and services purchased by tourists through the avatar guide. For example, the system records the sales of goods and services purchased by tourists through the avatar guide and increases the remuneration to the guide based on those sales. For example, the system determines the remuneration based on the sales of souvenirs and tickets purchased by tourists through the avatar guide. The system also analyzes the sales of goods and services purchased by tourists through the avatar guide and increases the remuneration to the guide based on those sales. For example, the system adjusts the remuneration based on the sales of restaurants and activities booked by tourists through the avatar guide. The system also collects the sales of goods and services purchased by tourists through the avatar guide and increases the remuneration to the guide based on those sales. For example, the system determines the remuneration based on the sales of tour packages and experience programs purchased by tourists through the avatar guide. In this way, the system can increase the remuneration to the guide based on the sales of goods and services purchased by tourists through the avatar guide.

[0040] The system uses generative AI to analyze the congestion situation at tourist destinations in real time and suggest optimal visit times for tourists. For example, the system uses generative AI to count the number of tourists using cameras and sensors installed at tourist destinations to analyze the congestion situation in real time. For example, cameras are installed at the entrances and key spots of tourist destinations to analyze the congestion situation. The system also uses generative AI to collect location information from tourists' smartphones and analyze the flow of people within the tourist destination to analyze the congestion situation in real time. For example, it analyzes which areas tourists are concentrating in and grasps the congestion situation. The system also uses generative AI to analyze admission ticket sales data at tourist destinations and predict the number of visitors to analyze the congestion situation in real time. For example, if a large number of tickets are sold during a specific time period, it predicts the congestion during that time period. This allows the system to use generative AI to analyze the congestion situation at tourist destinations in real time and suggest optimal visit times for tourists.

[0041] The system uses the generation AI to analyze tourist behavior data and suggest improvements to tourist destinations and new tourist spots. For example, the system uses the generation AI to analyze tourist behavior data and identify improvements to tourist destinations. For example, if tourists stay for a short time in a particular area, the system suggests improvements to that area. The system also uses the generation AI to analyze tourist behavior data and suggest new tourist spots. For example, it suggests new tourist spots near areas that tourists frequently visit. The system also uses the generation AI to analyze tourist behavior data and suggest improvements to tourist destinations and new tourist spots. For example, if tourists frequently use a particular route, a new tourist spot can be added to that route. This makes it possible to analyze tourist behavior data using the generation AI and suggest improvements to tourist destinations and new tourist spots.

[0042] The system can use generative AI to support environmental protection at tourist destinations and the promotion of sustainable tourism. For example, the system uses generative AI to analyze tourist behavior data and suggest environmentally friendly tourist routes to support environmental protection at tourist destinations. For example, the system can suggest routes that avoid nature reserves. The system can also use generative AI to analyze tourist consumption data and suggest local eco-friendly products and services to promote sustainable tourism at tourist destinations. For example, the system can suggest local organic foods and eco-tours. The system can also use generative AI to analyze tourist feedback and suggest improvements to environmentally friendly tourist destinations to support environmental protection at tourist destinations and the promotion of sustainable tourism. For example, if tourists rate environmentally friendly facilities or services, the system can suggest the expansion of those facilities or services. In this way, generative AI can be used to support environmental protection at tourist destinations and the promotion of sustainable tourism.

[0043] The system can use generation AI to propose sightseeing plans that take into account the tourist's health condition and physical strength. For example, the system uses generation AI to propose sightseeing plans that take into account the tourist's health condition and physical strength. For example, for tourists who are not confident in their physical strength, the system can propose sightseeing routes that can be completed in a short amount of time. The system also uses generation AI to propose sightseeing plans that take into account the tourist's health condition and physical strength. For example, for tourists who are in good health, the system can propose sightseeing routes that allow for long walking times. The system also uses generation AI to propose sightseeing plans that take into account the tourist's health condition and physical strength. For example, if a tourist has a chronic illness, the system can propose a sightseeing route that is not too strenuous. In this way, the system can use generation AI to propose sightseeing plans that take into account the tourist's health condition and physical strength.

[0044] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0045] The guided tour providing system can further include a health management unit that monitors the health condition of tourists. The health management unit collects vital data such as tourists' heart rate, number of steps, and body temperature, and monitors the health condition of tourists in real time. For example, if a tourist feels tired, the health management unit can suggest a break. The health management unit can also suggest a reasonable sightseeing route based on the tourist's health condition. For example, for tourists who are not confident in their physical strength, it can suggest a sightseeing route that can be completed in a short amount of time. Furthermore, the health management unit can suggest appropriate hydration and meal timings based on the tourist's health condition. This makes it possible to provide a comfortable guided tour while protecting the tourist's health.

[0046] The guided tour provision system can also be equipped with a congestion analysis unit that analyzes the congestion situation at tourist destinations in real time. The congestion analysis unit counts the number of tourists using cameras and sensors installed at tourist destinations. For example, cameras can be installed at the entrances and key spots of tourist destinations to analyze the congestion situation. The congestion analysis unit can also collect location information from tourists' smartphones and analyze the flow of people within the tourist destination. For example, it can analyze which areas tourists are concentrated in to understand the congestion situation. Furthermore, the congestion analysis unit can analyze the sales data of admission tickets to tourist destinations and predict the number of visitors. For example, if a large number of tickets are sold during a specific time period, it can predict the congestion situation during that time period. This makes it possible to suggest the optimal time to visit to tourists.

[0047] The guided tour providing system can further include an environmental protection unit that supports environmental protection of tourist destinations. The environmental protection unit analyzes tourist behavior data and suggests environmentally friendly tourist routes. For example, it suggests routes that avoid nature reserves. The environmental protection unit can also analyze tourist consumption data and suggest local eco-friendly products and services. For example, it can suggest local organic foods and eco-tours. Furthermore, the environmental protection unit can analyze tourist feedback and suggest improvements to environmentally friendly tourist destinations. For example, if tourists rate environmentally friendly facilities or services, it can suggest the expansion of those facilities and services. This can support environmental protection of tourist destinations and the promotion of sustainable tourism.

[0048] The guided tour providing system can further include a health planning unit that proposes a sightseeing plan that suits the tourist's health condition and physical strength. The health planning unit proposes a sightseeing plan that takes into account the tourist's health condition and physical strength. For example, for tourists who are not confident in their physical strength, it proposes a sightseeing route that can be completed in a short time. The health planning unit can also propose a sightseeing route that allows for long walking times for tourists who are in good health. Furthermore, if a tourist has a chronic illness, the health planning unit can also propose a sightseeing route that is not too strenuous. This makes it possible to propose a sightseeing plan that suits the tourist's health condition and physical strength.

[0049] The guided tour provision system can also be equipped with a congestion analysis unit that analyzes the congestion situation at tourist destinations in real time. The congestion analysis unit counts the number of tourists using cameras and sensors installed at tourist destinations. For example, cameras can be installed at the entrances and key spots of tourist destinations to analyze the congestion situation. The congestion analysis unit can also collect location information from tourists' smartphones and analyze the flow of people within the tourist destination. For example, it can analyze which areas tourists are concentrated in to understand the congestion situation. Furthermore, the congestion analysis unit can analyze the sales data of admission tickets to tourist destinations and predict the number of visitors. For example, if a large number of tickets are sold during a specific time period, it can predict the congestion situation during that time period. This makes it possible to suggest the optimal time to visit to tourists.

[0050] The guided tour providing system can further include an environmental protection unit that supports environmental protection of tourist destinations. The environmental protection unit analyzes tourist behavior data and suggests environmentally friendly tourist routes. For example, it suggests routes that avoid nature reserves. The environmental protection unit can also analyze tourist consumption data and suggest local eco-friendly products and services. For example, it can suggest local organic foods and eco-tours. Furthermore, the environmental protection unit can analyze tourist feedback and suggest improvements to environmentally friendly tourist destinations. For example, if tourists rate environmentally friendly facilities or services, it can suggest the expansion of those facilities and services. This can support environmental protection of tourist destinations and the promotion of sustainable tourism.

[0051] The guided tour providing system can further include a health planning unit that proposes a sightseeing plan that suits the tourist's health condition and physical strength. The health planning unit proposes a sightseeing plan that takes into account the tourist's health condition and physical strength. For example, for tourists who are not confident in their physical strength, it proposes a sightseeing route that can be completed in a short time. The health planning unit can also propose a sightseeing route that allows for long walking times for tourists who are in good health. Furthermore, if a tourist has a chronic illness, the health planning unit can also propose a sightseeing route that is not too strenuous. This makes it possible to propose a sightseeing plan that suits the tourist's health condition and physical strength.

[0052] The processing flow of the first embodiment will be briefly explained below.

[0053] Step 1: The guide learning unit learns the guide's gestures, speaking style, facial expressions, and story development. For example, the guide learning unit analyzes and learns the guide's movements and speaking style using video and audio data of the guide showing around tourist spots. It can also learn the guide's gestures and speaking style using video analysis, audio analysis, and facial expression recognition technology. It can also learn the guide's emotions and psychological state to create a more human-like guide. Step 2: The personalization provision unit provides a personalized guided tour based on the tourist's needs and interests, based on the information learned by the guide learning unit. For example, the generation AI provides detailed information about tourist destinations based on prompts that include the tourist's interests and needs. It can also analyze the tourist's past travel history and social media posts to achieve more accurate personalization. It can also collect real-time behavioral data on tourists and provide the most appropriate guided tour on the spot. Step 3: The avatar guide implementation unit guides tourists in real time based on the guided tour provided by the personalization provider. For example, tourists can tour tourist spots while interacting with the avatar guide via smartphone or tablet. The avatar guide can also analyze tourists' real-time reactions and respond immediately. Furthermore, the emotion estimation function can be used to adjust the guide's facial expressions and tone in real time according to the tourists' emotions.

[0054] (Example 2) The guided tour providing system according to an embodiment of the present invention is a system that uses generation AI to provide personalized guided tours to tourists visiting Japan. As a result, the guided tour providing system provides individually customized guided tours to tourists visiting Japan, and the guides themselves can receive compensation.

[0055] A guided tour providing system according to an embodiment includes a generation AI, a guide learning unit, a personalization providing unit, and an avatar guide implementation unit. The generation AI includes a guide learning unit that learns a guide's gestures, speaking style, facial expressions, and story development. For example, the guide learning unit analyzes and learns a guide's movements and speaking style using video and audio data of the guide guiding around tourist attractions. The guide learning unit can also learn a guide's gestures and speaking style using video analysis, audio analysis, and facial expression recognition technology. The guide learning unit can also learn a guide's emotions and psychological state to provide a more human-like guide. The personalization providing unit provides a personalized guided tour based on the information learned by the guide learning unit, tailored to the needs and interests of tourists. For example, the personalization providing unit uses prompts containing the tourist's interests and needs to provide detailed information about tourist attractions using the generation AI. The personalization providing unit can also analyze a tourist's past travel history and social media posts to achieve more accurate personalization. The personalization providing unit can also collect real-time behavioral data from tourists and provide the optimal guided tour on the spot. The avatar guide implementation unit provides real-time guidance to tourists based on the guided tour provided by the personalization provision unit. For example, the avatar guide implementation unit allows tourists to tour tourist attractions while interacting with the avatar guide via a smartphone or tablet. The avatar guide implementation unit can also analyze tourists' real-time reactions and respond immediately. The avatar guide implementation unit can also use an emotion estimation function to adjust the guide's facial expression and tone in real time according to the tourist's emotions. This allows the guided tour provision system according to the embodiment to provide individually customized guided tours to tourists visiting Japan, and the guide himself / herself can receive compensation. For example, the guide's compensation can be varied based on the tourist's satisfaction and feedback. The guide's compensation can also be increased or decreased based on the frequency of use and popularity of the avatar guide. The guide's compensation can also be increased based on sales of products and services purchased by tourists through the avatar guide.

[0056] The guide learning unit can also learn the guide's emotions and psychological state, enabling the creation of a more human-like guide. For example, the guide learning unit analyzes the guide's facial expressions and tone of voice to capture changes in emotions. For example, it learns the differences in facial expressions and voice when a guide is excited or relaxed. The guide learning unit also analyzes the guide's past behavioral data to extract reaction patterns in specific situations. For example, it learns how the guide responds to tourists' questions. The guide learning unit also collects the guide's self-reported data and quantifies changes in emotions. For example, it records the satisfaction and stress level the guide felt after the tour ended and uses that as learning data. In this way, by learning the guide's emotions and psychological state, the creation of a more human-like guide can be achieved.

[0057] The guide learning unit can add real-time reaction data of tourists and improve the adaptability of the guide. For example, the guide learning unit uses a smartphone or wearable device to analyze the facial expressions and voices of tourists to collect real-time reaction data of tourists. For example, it captures the moment when a tourist smiles or shows interest. The guide learning unit also uses cameras and microphones installed at tourist spots to record the behavior and reactions of tourists. For example, it analyzes how tourists react to particular exhibits. The guide learning unit also collects feedback from tourists in real time and improves the adaptability of the guide. For example, it records the impressions and opinions that tourists have during the tour in real time and uses them as learning data. In this way, the adaptability of the guide can be improved by adding real-time reaction data of tourists.

[0058] The guide learning unit can use the emotion estimation function to analyze the emotions of the guide and generate a guide who will express emotions optimally to tourists. The guide learning unit, for example, uses the emotion estimation function to analyze the emotions of the guide and generate a guide who will express emotions optimally to tourists. For example, when the guide is excited, the guide will speak to tourists in an energetic manner. The guide learning unit also uses the emotion estimation function to analyze the emotions of the guide and generate a guide who will express emotions optimally to tourists. For example, when the guide is relaxed, the guide will speak to tourists in a calm manner. The guide learning unit also uses the emotion estimation function to analyze the emotions of the guide and generate a guide who will express emotions optimally to tourists. For example, when the guide is nervous, the guide will speak to tourists in a calm manner. In this way, the emotion estimation function can be used to analyze the emotions of the guide and generate a guide who will express emotions optimally to tourists.

[0059] The personalization providing unit can analyze a tourist's past travel history or the content of their posts on social media to achieve more accurate personalization. The personalization providing unit, for example, analyzes a tourist's past travel history and has the generation AI learn from it. For example, a personalized guided tour is provided based on tourist attractions that the tourist has visited in the past and activities that the tourist has been interested in. The personalization providing unit also analyzes the content of tourist's posts on social media and has the generation AI learn from it. For example, it analyzes photos and comments posted by tourists on social media to identify tourist attractions and activities that the tourist is interested in. The personalization providing unit also integrates the tourist's past travel history and the content of their posts on social media and has the generation AI learn from it. For example, it achieves more accurate personalization based on tourist attractions that the tourist has visited in the past and the content that the tourist has posted on social media. In this way, more accurate personalization can be achieved by analyzing a tourist's past travel history and the content of their posts on social media.

[0060] The personalization provision unit collects real-time behavioral data of tourists and can provide the most suitable guided tour on the spot. For example, the personalization provision unit uses a smartphone or wearable device to collect real-time behavioral data of tourists and analyzes their location information and movement patterns. For example, the personalization provision unit provides the most suitable guided tour based on the tourist's current location. The personalization provision unit also uses sensors and cameras installed at tourist spots to record the tourist's behavior. For example, if a tourist shows interest in a particular exhibit, the personalization provision unit provides detailed information about that exhibit. The personalization provision unit also collects feedback from tourists in real time and provides the most suitable guided tour on the spot. For example, the personalization provision unit records the impressions and opinions that tourists have during the tour in real time and adjusts the guided tour based on that. In this way, by collecting real-time behavioral data of tourists, the most suitable guided tour can be provided on the spot.

[0061] The personalization providing unit can use the emotion estimation function to adjust the tour content in real time according to the tourist's emotion. The personalization providing unit, for example, uses the emotion estimation function to adjust the tour content in real time according to the tourist's emotion. For example, when the tourist is excited, an energetic activity is suggested. The personalization providing unit also uses the emotion estimation function to adjust the tour content in real time according to the tourist's emotion. For example, when the tourist is relaxed, a calm tourist spot is suggested. The personalization providing unit also uses the emotion estimation function to adjust the tour content in real time according to the tourist's emotion. For example, when the tourist is nervous, a tourist spot with a calm atmosphere is suggested. In this way, the emotion estimation function can be used to adjust the tour content in real time according to the tourist's emotion.

[0062] The avatar guide implementation unit can analyze tourists' real-time reactions and respond immediately. To analyze tourists' real-time reactions, the avatar guide implementation unit uses the camera and microphone of a smartphone or tablet to analyze their facial expressions and tone of voice. For example, it captures the moment a tourist smiles or shows interest. The avatar guide implementation unit also uses sensors and cameras installed at tourist spots to record tourists' behavior. For example, if a tourist shows interest in a particular exhibit, it provides detailed information about that exhibit. The avatar guide implementation unit also collects tourists' feedback in real time, allowing the avatar guide to respond immediately. For example, it records tourists' impressions and opinions during the tour in real time and adjusts the content of the guide based on that information. This allows the avatar guide implementation unit to analyze tourists' real-time reactions and respond immediately, thereby providing a more satisfying guide experience.

[0063] The avatar guide implementation unit can use the emotion estimation function to adjust the guide's facial expression and tone in real time according to the tourist's emotions. The avatar guide implementation unit, for example, uses the emotion estimation function to adjust the guide's facial expression and tone in real time according to the tourist's emotions. For example, when the tourist is excited, the guide will speak with an energetic expression and tone. The avatar guide implementation unit also uses the emotion estimation function to adjust the guide's facial expression and tone in real time according to the tourist's emotions. For example, when the tourist is relaxed, the guide will speak with a calm expression and tone. The avatar guide implementation unit also uses the emotion estimation function to adjust the guide's facial expression and tone in real time according to the tourist's emotions. For example, when the tourist is nervous, the guide will speak with a calm expression and tone. In this way, the emotion estimation function can be used to adjust the guide's facial expression and tone in real time according to the tourist's emotions.

[0064] The avatar guide implementation unit can combine AR technology to visually explain the history and culture of a tourist destination. For example, the avatar guide implementation unit uses AR technology to visually explain the history and culture of a tourist destination by using the camera of a smartphone or tablet to overlay digital information on real-world scenery. For example, it displays restored images of historical buildings. The avatar guide implementation unit also uses AR technology to develop a dedicated AR app to visually explain the history and culture of a tourist destination, allowing tourists to view the information through the app. For example, AR markers are placed at important points in a tourist destination, and information is displayed when the camera is pointed at them. The avatar guide implementation unit also uses AR technology to visually explain the history and culture of a tourist destination by using models and dioramas of the tourist destination and displaying detailed information using AR. For example, when the camera is pointed at a model, the internal structure and historical background of a building are displayed. In this way, the history and culture of a tourist destination can be visually explained by combining AR technology.

[0065] The avatar guide implementation unit combines voice recognition technology to provide instant answers to tourists' questions. The avatar guide implementation unit uses, for example, voice recognition technology to analyze tourists' voices using the microphone on a smartphone or tablet to provide instant answers to their questions. For example, the questions asked by tourists are converted into text in real time, and the generation AI generates answers. The avatar guide implementation unit also uses voice recognition technology to develop a dedicated voice recognition app to provide instant answers to tourists' questions, allowing tourists to ask questions through the app. For example, when a tourist speaks to the app, the generation AI immediately displays the answer. The avatar guide implementation unit also uses voice recognition technology to analyze tourists' questions using voice recognition devices installed at tourist spots to provide instant answers to their questions. For example, a voice recognition device is installed at an information center at a tourist spot, and when a tourist asks a question, the generation AI provides an answer. In this way, by combining voice recognition technology, tourists' questions can be answered instantly.

[0066] The avatar guide implementation unit uses the emotion estimation function to adjust the guide's speaking style and content based on the tourist's emotions, thereby providing a more satisfying guide experience. The avatar guide implementation unit, for example, uses the emotion estimation function to adjust the guide's speaking style and content based on the tourist's emotions. For example, when the tourist is excited, it provides an energetic speaking style and content. The avatar guide implementation unit also uses the emotion estimation function to adjust the guide's speaking style and content based on the tourist's emotions. For example, when the tourist is relaxed, it provides a calm speaking style and content. The avatar guide implementation unit also uses the emotion estimation function to adjust the guide's speaking style and content based on the tourist's emotions. For example, when the tourist is nervous, it provides a calm speaking style and content. In this way, the emotion estimation function can be used to adjust the guide's speaking style and content based on the tourist's emotions, thereby providing a more satisfying guide experience.

[0067] The system can vary the compensation paid to the guide based on the tourist's satisfaction and feedback. For example, the system collects tourist satisfaction and feedback and varies the compensation paid to the guide. For example, the system determines the compensation based on surveys and reviews filled out by tourists after the tour ends. The system also collects tourist satisfaction and feedback in real time and varies the compensation paid to the guide. For example, the system records the impressions and opinions of tourists during the tour in real time and adjusts the compensation based on this. The system also analyzes tourist satisfaction and feedback and varies the compensation paid to the guide. For example, the system analyzes photos and videos taken by tourists during the tour and evaluates the quality of the guide. This makes it possible to vary the compensation paid to the guide based on the tourist's satisfaction and feedback.

[0068] The system can increase or decrease the reward paid to the guide based on the frequency of use of the avatar guide and its popularity. For example, the system collects information on how often the avatar guide is used and increases or decreases the reward paid to the guide. For example, each time a tourist uses the avatar guide, the system records the number of times the avatar guide is used and determines the reward. The system also collects information on the popularity of the avatar guide and increases or decreases the reward paid to the guide. For example, if a tourist gives the avatar guide a high rating, the system adjusts the reward based on that rating. The system also analyzes the frequency of use and popularity of the avatar guide and increases or decreases the reward paid to the guide. For example, the system determines the reward based on the number of times a tourist uses the avatar guide and their rating. In this way, the system can increase or decrease the reward paid to the guide based on the frequency of use and popularity of the avatar guide.

[0069] The system can use the emotion estimation function to adjust the reward for the guide based on the tourist's emotions. For example, the system uses the emotion estimation function to adjust the reward for the guide based on the tourist's emotions. For example, the system determines the reward based on the emotion score felt by the tourist during the tour. The system also uses the emotion estimation function to adjust the reward for the guide based on the tourist's emotions. For example, if the tourist feels many positive emotions during the tour, the system increases the reward based on the emotion score. The system also uses the emotion estimation function to adjust the reward for the guide based on the tourist's emotions. For example, if the tourist feels few negative emotions during the tour, the system increases the reward based on the emotion score. In this way, the emotion estimation function can be used to adjust the reward for the guide based on the tourist's emotions.

[0070] The system can increase a guide's reward based on collaboration with other guides or the implementation of a joint tour. For example, the system increases the reward based on collaboration with other guides or the implementation of a joint tour. For example, if multiple guides jointly implement a tour, the reward is shared. The system also increases the reward based on collaboration with other guides or the implementation of a joint tour. For example, if a guide cooperates with other guides to implement a special event or tour, the reward is increased. The system also increases the reward based on collaboration with other guides or the implementation of a joint tour. For example, if a guide develops a new tour plan in collaboration with other guides, the reward is increased. In this way, a guide's reward can be increased based on collaboration with other guides or the implementation of a joint tour.

[0071] The system can increase the remuneration to the guide based on the sales of goods and services purchased by tourists through the avatar guide. For example, the system records the sales of goods and services purchased by tourists through the avatar guide and increases the remuneration to the guide based on those sales. For example, the system determines the remuneration based on the sales of souvenirs and tickets purchased by tourists through the avatar guide. The system also analyzes the sales of goods and services purchased by tourists through the avatar guide and increases the remuneration to the guide based on those sales. For example, the system adjusts the remuneration based on the sales of restaurants and activities booked by tourists through the avatar guide. The system also collects the sales of goods and services purchased by tourists through the avatar guide and increases the remuneration to the guide based on those sales. For example, the system determines the remuneration based on the sales of tour packages and experience programs purchased by tourists through the avatar guide. In this way, the system can increase the remuneration to the guide based on the sales of goods and services purchased by tourists through the avatar guide.

[0072] The system can use the emotion estimation function to adjust the reward to the guide in real time based on the tourist's emotions. For example, the system uses the emotion estimation function to adjust the reward to the guide in real time based on the tourist's emotions. For example, the system collects emotion scores felt by tourists during the tour in real time and determines the reward based on the emotion scores. The system also uses the emotion estimation function to adjust the reward to the guide in real time based on the tourist's emotions. For example, if tourists feel a lot of positive emotions during the tour, the system increases the reward based on the emotion score. The system also uses the emotion estimation function to adjust the reward to the guide in real time based on the tourist's emotions. For example, if tourists feel a little negative emotions during the tour, the system increases the reward based on the emotion score. In this way, the system can use the emotion estimation function to adjust the reward to the guide in real time based on the tourist's emotions.

[0073] The system uses generative AI to analyze the congestion situation at tourist destinations in real time and suggest optimal visit times for tourists. For example, the system uses generative AI to count the number of tourists using cameras and sensors installed at tourist destinations to analyze the congestion situation in real time. For example, cameras are installed at the entrances and key spots of tourist destinations to analyze the congestion situation. The system also uses generative AI to collect location information from tourists' smartphones and analyze the flow of people within the tourist destination to analyze the congestion situation in real time. For example, it analyzes which areas tourists are concentrating in and grasps the congestion situation. The system also uses generative AI to analyze admission ticket sales data at tourist destinations and predict the number of visitors to analyze the congestion situation in real time. For example, if a large number of tickets are sold during a specific time period, it predicts the congestion during that time period. This allows the system to use generative AI to analyze the congestion situation at tourist destinations in real time and suggest optimal visit times for tourists.

[0074] The system uses the generation AI to analyze tourist behavior data and suggest improvements to tourist destinations and new tourist spots. For example, the system uses the generation AI to analyze tourist behavior data and identify improvements to tourist destinations. For example, if tourists stay for a short time in a particular area, the system suggests improvements to that area. The system also uses the generation AI to analyze tourist behavior data and suggest new tourist spots. For example, it suggests new tourist spots near areas that tourists frequently visit. The system also uses the generation AI to analyze tourist behavior data and suggest improvements to tourist destinations and new tourist spots. For example, if tourists frequently use a particular route, a new tourist spot can be added to that route. This makes it possible to analyze tourist behavior data using the generation AI and suggest improvements to tourist destinations and new tourist spots.

[0075] The system can use the emotion estimation function to suggest improvements to services and facilities at tourist destinations based on the emotions of tourists. For example, the system uses the emotion estimation function to suggest improvements to services at tourist destinations based on the emotions of tourists. For example, if a tourist expresses negative emotions toward a particular service, the system suggests improvements to that service. The system also uses the emotion estimation function to suggest improvements to facilities at tourist destinations based on the emotions of tourists. For example, if a tourist expresses positive emotions toward a particular facility, the system suggests expanding that facility. The system also uses the emotion estimation function to suggest improvements to services and facilities at tourist destinations based on the emotions of tourists. For example, if a tourist expresses negative emotions toward a particular area, the system suggests improvements to that area. In this way, the emotion estimation function can be used to suggest improvements to services and facilities at tourist destinations based on the emotions of tourists.

[0076] The system can use generative AI to support environmental protection at tourist destinations and the promotion of sustainable tourism. For example, the system uses generative AI to analyze tourist behavior data and suggest environmentally friendly tourist routes to support environmental protection at tourist destinations. For example, the system can suggest routes that avoid nature reserves. The system can also use generative AI to analyze tourist consumption data and suggest local eco-friendly products and services to promote sustainable tourism at tourist destinations. For example, the system can suggest local organic foods and eco-tours. The system can also use generative AI to analyze tourist feedback and suggest improvements to environmentally friendly tourist destinations to support environmental protection at tourist destinations and the promotion of sustainable tourism. For example, if tourists rate environmentally friendly facilities or services, the system can suggest the expansion of those facilities or services. In this way, generative AI can be used to support environmental protection at tourist destinations and the promotion of sustainable tourism.

[0077] The system can use generation AI to propose sightseeing plans that take into account the tourist's health condition and physical strength. For example, the system uses generation AI to propose sightseeing plans that take into account the tourist's health condition and physical strength. For example, for tourists who are not confident in their physical strength, the system can propose sightseeing routes that can be completed in a short amount of time. The system also uses generation AI to propose sightseeing plans that take into account the tourist's health condition and physical strength. For example, for tourists who are in good health, the system can propose sightseeing routes that allow for long walking times. The system also uses generation AI to propose sightseeing plans that take into account the tourist's health condition and physical strength. For example, if a tourist has a chronic illness, the system can propose a sightseeing route that is not too strenuous. In this way, the system can use generation AI to propose sightseeing plans that take into account the tourist's health condition and physical strength.

[0078] The system can use the emotion estimation function to optimize a marketing strategy for a tourist destination based on the emotions of tourists. For example, the system uses the emotion estimation function to optimize a marketing strategy for a tourist destination based on the emotions of tourists. For example, if tourists express positive emotions toward a particular marketing campaign, the system strengthens the campaign. The system also uses the emotion estimation function to optimize a marketing strategy for a tourist destination based on the emotions of tourists. For example, if tourists express negative emotions toward a particular advertisement, the system improves the advertisement. The system also uses the emotion estimation function to optimize a marketing strategy for a tourist destination based on the emotions of tourists. For example, if tourists express positive emotions toward a particular promotion, the system expands the promotion. In this way, the emotion estimation function can be used to optimize a marketing strategy for a tourist destination based on the emotions of tourists.

[0079] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0080] The guided tour providing system can further include a health management unit that monitors the health condition of tourists. The health management unit collects vital data such as tourists' heart rate, number of steps, and body temperature, and monitors the health condition of tourists in real time. For example, if a tourist feels tired, the health management unit can suggest a break. The health management unit can also suggest a reasonable sightseeing route based on the tourist's health condition. For example, for tourists who are not confident in their physical strength, it can suggest a sightseeing route that can be completed in a short amount of time. Furthermore, the health management unit can suggest appropriate hydration and meal timings based on the tourist's health condition. This makes it possible to provide a comfortable guided tour while protecting the tourist's health.

[0081] The guided tour provision system can also be equipped with a congestion analysis unit that analyzes the congestion situation at tourist destinations in real time. The congestion analysis unit counts the number of tourists using cameras and sensors installed at tourist destinations. For example, cameras can be installed at the entrances and key spots of tourist destinations to analyze the congestion situation. The congestion analysis unit can also collect location information from tourists' smartphones and analyze the flow of people within the tourist destination. For example, it can analyze which areas tourists are concentrated in to understand the congestion situation. Furthermore, the congestion analysis unit can analyze the sales data of admission tickets to tourist destinations and predict the number of visitors. For example, if a large number of tickets are sold during a specific time period, it can predict the congestion situation during that time period. This makes it possible to suggest the optimal time to visit to tourists.

[0082] The guided tour providing system can further include an environmental protection unit that supports environmental protection of tourist destinations. The environmental protection unit analyzes tourist behavior data and suggests environmentally friendly tourist routes. For example, it suggests routes that avoid nature reserves. The environmental protection unit can also analyze tourist consumption data and suggest local eco-friendly products and services. For example, it can suggest local organic foods and eco-tours. Furthermore, the environmental protection unit can analyze tourist feedback and suggest improvements to environmentally friendly tourist destinations. For example, if tourists rate environmentally friendly facilities or services, it can suggest the expansion of those facilities and services. This can support environmental protection of tourist destinations and the promotion of sustainable tourism.

[0083] The guided tour providing system can further include a health planning unit that proposes a sightseeing plan that suits the tourist's health condition and physical strength. The health planning unit proposes a sightseeing plan that takes into account the tourist's health condition and physical strength. For example, for tourists who are not confident in their physical strength, it proposes a sightseeing route that can be completed in a short time. The health planning unit can also propose a sightseeing route that allows for long walking times for tourists who are in good health. Furthermore, if a tourist has a chronic illness, the health planning unit can also propose a sightseeing route that is not too strenuous. This makes it possible to propose a sightseeing plan that suits the tourist's health condition and physical strength.

[0084] The guided tour providing system can further include a marketing optimization unit that optimizes the marketing strategy of the tourist destination. The marketing optimization unit optimizes the marketing strategy of the tourist destination based on the emotions of tourists. For example, if tourists express positive emotions toward a particular marketing campaign, the marketing optimization unit strengthens the campaign. The marketing optimization unit can also improve a particular advertisement if tourists express negative emotions toward the advertisement. Furthermore, the marketing optimization unit can expand a particular promotion if tourists express positive emotions toward the promotion. This allows the marketing strategy of the tourist destination to be optimized.

[0085] The guided tour providing system can further include a service improvement unit that suggests improvements to services and facilities at tourist destinations. The service improvement unit suggests improvements to services at tourist destinations based on tourist emotions. For example, if a tourist expresses negative emotions toward a particular service, the service improvement unit can suggest improvements to that service. In addition, if a tourist expresses positive emotions toward a particular facility, the service improvement unit can also suggest expansion of that facility. Furthermore, if a tourist expresses negative emotions toward a particular area, the service improvement unit can also suggest improvements to that area. This makes it possible to suggest improvements to services and facilities at tourist destinations.

[0086] The guided tour provision system can also be equipped with a congestion analysis unit that analyzes the congestion situation at tourist destinations in real time. The congestion analysis unit counts the number of tourists using cameras and sensors installed at tourist destinations. For example, cameras can be installed at the entrances and key spots of tourist destinations to analyze the congestion situation. The congestion analysis unit can also collect location information from tourists' smartphones and analyze the flow of people within the tourist destination. For example, it can analyze which areas tourists are concentrated in to understand the congestion situation. Furthermore, the congestion analysis unit can analyze the sales data of admission tickets to tourist destinations and predict the number of visitors. For example, if a large number of tickets are sold during a specific time period, it can predict the congestion situation during that time period. This makes it possible to suggest the optimal time to visit to tourists.

[0087] The guided tour providing system can further include an environmental protection unit that supports environmental protection of tourist destinations. The environmental protection unit analyzes tourist behavior data and suggests environmentally friendly tourist routes. For example, it suggests routes that avoid nature reserves. The environmental protection unit can also analyze tourist consumption data and suggest local eco-friendly products and services. For example, it can suggest local organic foods and eco-tours. Furthermore, the environmental protection unit can analyze tourist feedback and suggest improvements to environmentally friendly tourist destinations. For example, if tourists rate environmentally friendly facilities or services, it can suggest the expansion of those facilities and services. This can support environmental protection of tourist destinations and the promotion of sustainable tourism.

[0088] The guided tour providing system can further include a health planning unit that proposes a sightseeing plan that suits the tourist's health condition and physical strength. The health planning unit proposes a sightseeing plan that takes into account the tourist's health condition and physical strength. For example, for tourists who are not confident in their physical strength, it proposes a sightseeing route that can be completed in a short time. The health planning unit can also propose a sightseeing route that allows for long walking times for tourists who are in good health. Furthermore, if a tourist has a chronic illness, the health planning unit can also propose a sightseeing route that is not too strenuous. This makes it possible to propose a sightseeing plan that suits the tourist's health condition and physical strength.

[0089] The guided tour providing system can further include a marketing optimization unit that optimizes the marketing strategy of the tourist destination. The marketing optimization unit optimizes the marketing strategy of the tourist destination based on the emotions of tourists. For example, if tourists express positive emotions toward a particular marketing campaign, the marketing optimization unit strengthens the campaign. The marketing optimization unit can also improve a particular advertisement if tourists express negative emotions toward the advertisement. Furthermore, the marketing optimization unit can expand a particular promotion if tourists express positive emotions toward the promotion. This allows the marketing strategy of the tourist destination to be optimized.

[0090] The processing flow of the second embodiment will be briefly explained below.

[0091] Step 1: The guide learning unit learns the guide's gestures, speaking style, facial expressions, and story development. For example, the guide learning unit analyzes and learns the guide's movements and speaking style using video and audio data of the guide showing around tourist spots. It can also learn the guide's gestures and speaking style using video analysis, audio analysis, and facial expression recognition technology. It can also learn the guide's emotions and psychological state to create a more human-like guide. Step 2: The personalization provision unit provides a personalized guided tour based on the tourist's needs and interests, based on the information learned by the guide learning unit. For example, the generation AI provides detailed information about tourist destinations based on prompts that include the tourist's interests and needs. It can also analyze the tourist's past travel history and social media posts to achieve more accurate personalization. It can also collect real-time behavioral data on tourists and provide the most appropriate guided tour on the spot. Step 3: The avatar guide implementation unit guides tourists in real time based on the guided tour provided by the personalization provider. For example, tourists can tour tourist spots while interacting with the avatar guide via smartphone or tablet. The avatar guide can also analyze tourists' real-time reactions and respond immediately. Furthermore, the emotion estimation function can be used to adjust the guide's facial expressions and tone in real time according to the tourists' emotions.

[0092] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0093] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0094] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0095] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0096] 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.

[0097] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0098] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0099] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0100] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0101] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0102] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0103] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0104] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0105] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0106] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0107] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0108] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0109] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0110] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0111] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0112] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0113] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0114] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0115] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0116] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0117] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0118] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0119] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0120] In the headset type terminal 314, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0121] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0122] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0123] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0124] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0125] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0126] 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.

[0127] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0128] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0129] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0130] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0131] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0132] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0133] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0134] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0135] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0136] In the robot 414, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The robot 414 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0137] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0138] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0139] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0140] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0141] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0142] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0143] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0144] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0145] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0146] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0147] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0148] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0149] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0150] 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.

[0151] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0152] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0153] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0154] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0155] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0156] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0157] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0158] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0159] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. Equipped with generative AI, The generated AI is The guide learning section teaches guide gestures, speaking styles, facial expressions, and how to develop a story. a personalization providing unit that provides a personalized guided tour according to the needs and interests of tourists based on the information learned by the guide learning unit; and an avatar guide implementation unit that provides real-time guidance to tourists based on the guided tour provided by the personalization providing unit. A system characterized by:

2. The guide learning unit It also learns the emotions and psychological state of the guide, making it more human-like.

2. The system of claim 1.

3. The guide learning unit Add real-time reaction data from the tourists to improve the adaptability of the guide 2. The system of claim 1.

4. The guide learning unit Analyzing the emotions of the guide and generating a guide who expresses the most appropriate emotions to the tourist 2. The system of claim 1.

5. The personalization providing unit Analyzing the tourist's past travel history or social media posts will enable more accurate personalization.

2. The system of claim 1.

6. The personalization providing unit Collect real-time behavioral data of the tourists and provide them with the most suitable guided tours on the spot. The system of claim 1 .

7. The personalization providing unit To adjust tour contents in real time according to the emotions of the tourists. The system of claim 1 .

8. The avatar guide mounting unit is Analyzing the real-time reactions of the tourists and responding immediately The system of claim 1 .

Images