System

Abstract

An object of a system according to an embodiment is to provide storytelling and an AR experience in accordance with the level of a visitor.SOLUTION: A system includes a determination unit, a storytelling unit, a provision unit, and an analysis unit. The determination unit determines the level of the visitor. The storytelling section performs storytelling on the basis of the level determined by the determination section. The provision unit provides an AR experience on the basis of the storytelling provided by the storytelling unit. The analysis unit analyzes the reaction of the visitor to the AR experience provided by the 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] With conventional technology, it is difficult to provide appropriate cultural and historical information according to the visitor's level, which can lead to a decline in the quality of the experience.

[0005] The system according to the embodiment aims to provide storytelling and AR experiences tailored to the visitor's level. [Means for solving the problem]

[0006] A system according to an embodiment includes a determination unit, a storytelling unit, a provision unit, and an analysis unit. The determination unit determines a level of a visitor. The storytelling unit performs storytelling based on the level determined by the determination unit. The provision unit provides an AR experience based on the storytelling provided by the storytelling unit. The analysis unit analyzes the visitor's response to the AR experience provided by the provision unit. [Effects of the Invention]

[0007] The system according to the embodiment can provide storytelling and AR experiences tailored to the visitor's level. [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) A system according to an embodiment of the present invention allows visitors to understand and experience the culture and history of a region. The system determines the level of the visitor, performs storytelling based on that level, provides an AR experience, and analyzes the visitor's response. For example, the system uses AI that has learned information about the history and culture of a tourist destination to provide storytelling tailored to the visitor's level. Then, using AR technology, the system provides an experience that makes the visitor feel as if they are actually in the location. This allows visitors to gain a deeper understanding of the local culture and history and to have an engaging experience. This allows the system to deepen the visitor's understanding and experience. For example, when visiting a historical building, the visitor can learn more about the building's history and background. Also, when participating in a cultural event, the visitor can understand the significance and background of the event. This allows the visitor to more deeply appreciate the charm of the region.

[0029] A cultural experience system according to an embodiment includes a determination unit, a storytelling unit, a provision unit, and an analysis unit. The determination unit determines the level of a visitor. Examples of visitor levels include, but are not limited to, beginner, intermediate, and advanced. The determination unit can determine the level based on, for example, the visitor's age and interests. The determination unit can also estimate the visitor's emotions and adjust the level determination criteria based on the estimated emotions. For example, if the visitor is excited, a level that provides more detailed and stimulating information can be selected. The storytelling unit uses a generation AI to perform storytelling based on the level determined by the determination unit. Storytelling can be performed based on, for example, the structure of the story and the media used, but is not limited to, examples. For example, the generation AI uses a text generation AI (e.g., LLM) to perform storytelling according to the visitor's level. The storytelling unit can also estimate the visitor's emotions and adjust the way the storytelling is expressed based on the estimated emotions. For example, if the visitor is excited, storytelling can be performed with visually stimulating effects. The providing unit provides an AR experience based on the storytelling provided by the storytelling unit. AR experiences include, but are not limited to, those provided through a smartphone or tablet. For example, the providing unit can point a camera at a specific location and display information related to that location. The providing unit can also estimate a visitor's emotions and adjust how the AR experience is provided based on the estimated emotions. For example, if the visitor is excited, a visually stimulating AR experience can be provided. The analysis unit analyzes the visitor's reaction to the AR experience provided by the providing unit. The visitor's reaction can include, but is not limited to, facial expressions, actions, feedback, etc. For example, the analysis unit can analyze the visitor's reaction in real time and provide feedback to the storytelling unit. This allows the cultural experience system according to the embodiment to deepen the visitor's understanding and experience. For example, the content of the storytelling can be adjusted based on the visitor's reaction analyzed in real time.

[0030] The determination unit can determine the level based on the visitor's age or interests. The determination unit can determine the level based on, for example, the visitor's age. For example, the level can be set according to age groups such as children, adolescents, adults, and the elderly. The determination unit can also determine the level based on the visitor's interests. For example, the level can be set based on the visitor's hobbies, areas of interest, past behavioral history, etc. This makes it possible to determine the level according to the visitor's age and interests. Some or all of the above-described processing in the determination unit can be performed using, or without, AI, for example. For example, the determination unit can input data regarding the visitor's age and interests into the generation AI and have the generation AI determine the level.

[0031] The storytelling unit can perform storytelling according to the visitor's level. The storytelling unit, for example, adjusts the content of the storytelling according to the visitor's level. For example, the storytelling unit can explain things in simple terms to beginners, provide detailed information to intermediate visitors, and provide storytelling including specialized content to advanced visitors. The storytelling unit can also analyze the visitor's response in real time and adjust the content of the storytelling. For example, if the visitor shows interest, the storytelling unit can provide more detailed information, and if the visitor loses interest, the storytelling unit can switch to a simple explanation. This enables appropriate storytelling according to the visitor's level. Some or all of the above-described processing in the storytelling unit may be performed using, or without, a generation AI. For example, the storytelling unit can input data regarding the visitor's level into the generation AI and have the generation AI execute the storytelling content.

[0032] The providing unit can provide an AR experience through a smartphone or tablet. The providing unit provides the AR experience using, for example, a smartphone or tablet. For example, when a visitor points the smartphone camera at a specific location, information related to that location is displayed. The providing unit can also estimate the visitor's emotions and adjust the way the AR experience is provided based on the estimated emotions. For example, if the visitor is excited, a visually stimulating AR experience can be provided, and if the visitor is relaxed, a calm AR experience can be provided. This allows the visitor to enjoy the AR experience through the smartphone or tablet. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input data acquired by the smartphone or tablet into a generating AI and cause the generating AI to provide the AR experience.

[0033] The analysis unit can analyze the visitor's reactions in real time and provide feedback to the storytelling unit. For example, the analysis unit captures the visitor's facial expressions and behavior with a camera and analyzes them in real time. For example, the analysis unit calculates an emotion score based on changes in the visitor's facial expressions and provides feedback to the storytelling unit. The analysis unit can also record the visitor's voice and estimate their emotions using voice analysis technology. For example, the analysis unit can analyze the tone and speed of the visitor's voice and calculate an emotion score. Furthermore, the analysis unit can collect the visitor's biometric data (heart rate and electrodermal activity) using a sensor and estimate their emotions using an emotion estimation algorithm. For example, the analysis unit can calculate an emotion score based on heart rate fluctuations. This allows the visitor's reactions to be analyzed in real time and the content of the storytelling to be adjusted. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit can input image data of a visitor taken with a camera into the generation AI and have the generation AI estimate the visitor's emotions.

[0034] The providing unit can display information related to a specific location when a visitor points a camera at the location. For example, when a visitor points a smartphone or tablet camera at a specific location, the providing unit displays information related to the location. For example, when a visitor points a camera at a historical building, information about the building's history and background is displayed. The providing unit can also estimate the visitor's emotions and adjust the way the AR experience is provided based on the estimated emotions. For example, if the visitor is excited, a visually stimulating AR experience can be provided, and if the visitor is relaxed, a calming AR experience can be provided. This allows the visitor to obtain information related to the location by pointing the camera at the specific location. Some or all of the above-described processing by the providing unit may be performed using, for example, AI, or may be performed without AI. For example, the providing unit can input data acquired by the camera into a generating AI and cause the generating AI to display related information.

[0035] The determination unit can analyze the visitor's past visit history and select an appropriate level determination method. For example, the determination unit selects a level that the visitor is likely to be interested in based on information about places the visitor has visited in the past. For example, the determination unit analyzes the history of tourist spots the visitor has visited and events the visitor has participated in in the past and selects a relevant level. The determination unit can also analyze the visitor's past feedback and select an optimal level. For example, the level determination method can be adjusted based on feedback the visitor has provided in the past. This makes it possible to determine the level based on the visitor's past visit history. Some or all of the above-mentioned processing in the determination unit may be performed using, for example, AI, or may be performed without using AI. For example, the determination unit can input the visitor's past visit history data into the generation AI and have the generation AI determine the level.

[0036] The determination unit can determine the level based on the visitor's current interests and areas of interest. The determination unit selects the level based on, for example, themes in which the visitor is currently interested. For example, the determination unit selects a relevant level based on keywords recently searched by the visitor or the content of events the visitor is attending. The determination unit can also determine the level based on the visitor's recent behavioral history and survey results. This makes it possible to determine the level according to the visitor's current interests and areas of interest. Some or all of the above-mentioned processing in the determination unit may be performed using, for example, AI, or may be performed without using AI. For example, the determination unit can input data on the visitor's current interests and areas of interest to the generation AI and have the generation AI determine the level.

[0037] The determination unit can select an appropriate level determination means depending on the visitor's input method. For example, if the visitor uses voice input, the determination unit determines the level based on voice analysis. For example, the determination unit analyzes the visitor's voice data and selects an appropriate level. Furthermore, if the visitor uses text input, the determination unit can also determine the level based on text analysis. For example, the determination unit analyzes the visitor's text data and selects an appropriate level. Furthermore, if the visitor uses image input, the determination unit can also determine the level based on image analysis. For example, the determination unit analyzes the visitor's image data and selects an appropriate level. This makes it possible to determine the level depending on the visitor's input method. Some or all of the above-mentioned processing in the determination unit may be performed using, for example, AI, or may be performed without using AI. For example, the determination unit can input the visitor's input data to a generation AI and have the generation AI determine the level.

[0038] The determination unit can prioritize determining a level with a high relevance based on the visitor's geographical location information. For example, the determination unit can prioritize providing information related to the visitor's current location. For example, the determination unit can determine the level based on information related to the tourist spot where the visitor is currently located. The determination unit can also prioritize providing information related to tourist spots near the visitor. For example, the determination unit can determine the level based on information about tourist spots near the visitor. The determination unit can also prioritize providing information related to places the visitor has visited in the past. For example, the determination unit can determine the level based on information about tourist spots the visitor has visited in the past. This makes it possible to determine the level based on the visitor's geographical location information. Some or all of the above-described processing in the determination unit can be performed using, or without, AI. For example, the determination unit can input the visitor's geographical location information data to the generation AI and have the generation AI determine the level.

[0039] The determination unit can analyze the visitor's social media activity and determine the relevant level. For example, the determination unit provides information related to places where the visitor has checked in on social media. For example, the level is determined based on information about tourist spots where the visitor has checked in on social media. The determination unit can also analyze the content of the visitor's posts on social media and determine the relevant level. For example, the level is determined based on the content of the visitor's posts. Furthermore, the determination unit can determine the relevant level by referring to the activities of the visitor's friends on social media. For example, the level is determined based on information about places where the visitor's friends have checked in on social media. This makes it possible to determine the level based on the visitor's social media activity. Some or all of the above-described processing in the determination unit may be performed using, for example, AI, or may be performed without using AI. For example, the determination unit can input the visitor's social media activity data to the generation AI and have the generation AI determine the level.

[0040] The determination unit can customize the level determination method by reflecting the visitor's past feedback. The determination unit, for example, adjusts the level determination method based on feedback provided by the visitor in the past. For example, it analyzes the visitor's past feedback and provides a specific level preferentially. The determination unit can also select the optimal level determination method by referring to the visitor's past feedback. For example, it customizes the level determination method based on the visitor's past feedback. This makes it possible to determine the level based on the visitor's past feedback. Some or all of the above-described processing in the determination unit may be performed using AI, for example, or may be performed without using AI. For example, the determination unit can input the visitor's past feedback data into the generation AI and have the generation AI perform level determination.

[0041] The storytelling unit can adjust the level of detail in the storytelling based on the importance of the information. For example, the storytelling unit provides detailed explanations for important historical events. For example, detailed storytelling is performed for information in which visitors are particularly interested. The storytelling unit can also provide brief explanations for general information. For example, brief storytelling is performed for information in which visitors are not particularly interested. This enables the level of detail in the storytelling to be adjusted according to the importance of the information. Some or all of the above-mentioned processing in the storytelling unit may be performed using, or without, the generation AI. For example, the storytelling unit can input data regarding the importance of the information into the generation AI and have the generation AI execute the level of detail in the storytelling.

[0042] The storytelling unit can apply different storytelling algorithms depending on the category of information. For example, for historical information, the storytelling unit performs storytelling in chronological order. For example, if a visitor is interested in historical events, the storytelling unit performs storytelling in chronological order. The storytelling unit can also perform storytelling by theme for cultural information. For example, if a visitor is interested in cultural events, the storytelling unit can perform storytelling by theme. The storytelling unit can also perform storytelling by location for tourist attraction information. For example, if a visitor is interested in a particular tourist attraction, the storytelling unit can perform storytelling by location. This enables storytelling according to the category of information. Some or all of the above-described processing in the storytelling unit may be performed using, or without, a generation AI. For example, the storytelling unit can input data regarding the category of information into the generation AI and cause the generation AI to apply the storytelling algorithm.

[0043] The storytelling unit can improve the accuracy of storytelling by referring to the visitor's past storytelling results. The storytelling unit, for example, adjusts the content of the storytelling based on information that the visitor has been interested in in the past. For example, the storytelling is performed based on a theme that the visitor has shown interest in in the past. The storytelling unit can also analyze the visitor's past responses and select the optimal storytelling method. For example, the storytelling content can be adjusted based on the visitor's past response data. Furthermore, the storytelling unit can improve the accuracy of storytelling by referring to the visitor's past feedback. For example, the storytelling content can be adjusted based on the visitor's past feedback. This makes it possible to improve the accuracy of storytelling based on the visitor's past storytelling results. Some or all of the above-described processing in the storytelling unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the storytelling unit can input the visitor's past storytelling result data into the generation AI and cause the generation AI to improve the accuracy of storytelling.

[0044] The storytelling unit can determine the priority of storytelling based on the time when information is provided. For example, the storytelling unit can provide important information first to attract the visitor's interest. For example, information that is likely to be of particular interest to the visitor can be provided first. The storytelling unit can also postpone general information and prioritize providing important information. For example, information that the visitor is not particularly interested in can be postponed. Furthermore, the storytelling unit can provide information that is likely to be of interest to the visitor first to continue to attract their interest. For example, information that is likely to be of interest to the visitor can be provided preferentially. This enables the priority of storytelling to be determined according to the time when information is provided. Some or all of the above-described processing in the storytelling unit may be performed using, or without, the generation AI. For example, the storytelling unit can input data regarding the time when information is provided to the generation AI and have the generation AI execute the storytelling priority.

[0045] The storytelling unit can adjust the order of storytelling based on the relevance of the information. For example, the storytelling unit may provide related information in succession to deepen the visitor's understanding. For example, a visitor's understanding deepens when they receive related information in succession. The storytelling unit can also postpone less relevant information and prioritize providing important information. For example, it may postpone information that the visitor is less interested in. Furthermore, the storytelling unit can provide information that is likely to interest the visitor first to keep them interested. For example, it prioritizes providing information that is likely to interest the visitor. This enables the order of storytelling to be based on the relevance of the information. Some or all of the above-described processing in the storytelling unit may be performed using, or without, a generation AI. For example, the storytelling unit may input data regarding the relevance of the information into the generation AI and have the generation AI execute the order of storytelling.

[0046] The storytelling unit can adjust the use of technical terms in the storytelling according to the visitor's level of expertise. For example, the storytelling unit uses simple language to explain things to visitors with little expertise. For example, if the visitor is a beginner, the storytelling unit uses simple language to explain things. The storytelling unit can also use detailed technical terms to explain things to visitors with expertise. For example, if the visitor is an expert, the storytelling unit uses detailed technical terms to explain things. Furthermore, the storytelling unit can adjust the use of technical terms according to the visitor's level of understanding. For example, the use of technical terms is adjusted according to the visitor's level of understanding. This enables storytelling according to the visitor's level of expertise. Some or all of the above-mentioned processing in the storytelling unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the storytelling unit can input data regarding the visitor's level of expertise into the generation AI and have the generation AI execute the use of technical terms.

[0047] When providing an AR experience, the provision unit can select the optimal provision method by referring to the visitor's past experience history. For example, the provision unit selects the optimal provision method based on the visitor's past AR experiences. For example, the provision unit selects the provision method based on the visitor's past AR experience history. The provision unit can also select the optimal provision method by referring to the visitor's past feedback. For example, the provision unit selects the provision method based on the visitor's past feedback. Furthermore, the provision unit can analyze the visitor's past experience history and select the optimal provision method. For example, the provision unit selects the provision method based on the visitor's past experience history. This makes it possible to provide an AR experience based on the visitor's past experience history. Some or all of the above-described processing in the provision unit may be performed using, for example, AI, or may be performed without using AI. For example, the provision unit can input the visitor's past experience history data into the generation AI and cause the generation AI to execute the optimal provision method.

[0048] When providing an AR experience, the provision unit can customize the content to be provided based on the visitor's current interests and areas of interest. For example, the provision unit provides an AR experience based on a theme that the visitor is currently interested in. For example, the provision unit can provide a related AR experience based on keywords recently searched by the visitor. The provision unit can also provide an AR experience based on the content of an event in which the visitor is currently participating. For example, the provision unit can provide an AR experience related to the event in which the visitor is currently participating. This makes it possible to provide an AR experience that suits the visitor's current interests and areas of interest. Some or all of the above-described processing by the provision unit may be performed using, or without, AI. For example, the provision unit can input data related to the visitor's current interests and areas of interest into a generation AI and cause the generation AI to customize the content to be provided.

[0049] The provision unit can improve the provision method by reflecting visitor feedback when providing an AR experience. The provision unit, for example, adjusts the content of the AR experience based on visitor feedback. For example, the provision unit analyzes visitor feedback and provides an AR experience that emphasizes specific elements. The provision unit can also select the optimal provision method by referring to visitor feedback. For example, the provision method can be improved based on visitor feedback. This makes it possible to improve the provision method of the AR experience based on visitor feedback. Some or all of the above-mentioned processing in the provision unit may be performed using AI, for example, or may be performed without using AI. For example, the provision unit can input visitor feedback data into a generation AI and cause the generation AI to improve the provision method.

[0050] When providing an AR experience, the providing unit can select the optimal providing method by taking into consideration the visitor's geographical location information. The providing unit, for example, provides an AR experience related to the location where the visitor is currently located. For example, it provides an AR experience related to a tourist spot where the visitor is currently located. The providing unit can also provide an AR experience related to a tourist spot near the visitor. For example, it can provide an AR experience related to a tourist spot near the visitor. Furthermore, the providing unit can also provide an AR experience related to a location that the visitor has visited in the past. For example, it can provide an AR experience related to a tourist spot that the visitor has visited in the past. This makes it possible to provide an AR experience based on the visitor's geographical location information. Some or all of the above-described processing by the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input the visitor's geographical location information data to the generation AI and cause the generation AI to execute the optimal providing method.

[0051] When providing an AR experience, the providing unit can analyze the visitor's social media activity and suggest content to be provided. For example, the providing unit can provide an AR experience related to a place where the visitor has checked in on social media. For example, the providing unit can provide an AR experience related to a tourist spot where the visitor has checked in on social media. The providing unit can also analyze the visitor's social media posts and provide a related AR experience. For example, the providing unit can provide an AR experience based on the visitor's posts. Furthermore, the providing unit can provide a related AR experience by referring to the activities of the visitor's friends on social media. For example, the providing unit can provide an AR experience related to a place where the visitor's friends have checked in on social media. This makes it possible to provide an AR experience based on the visitor's social media activity. Some or all of the above-described processing by the providing unit may be performed using, or without, AI. For example, the providing unit can input the visitor's social media activity data into a generation AI and cause the generation AI to suggest content to be provided.

[0052] When providing an AR experience, the providing unit can customize the providing method by reflecting the visitor's past feedback. The providing unit, for example, adjusts the content of the AR experience based on the visitor's past feedback. For example, the providing unit analyzes the visitor's past feedback and provides an AR experience that emphasizes specific elements. The providing unit can also select the optimal providing method by referring to the visitor's past feedback. For example, the providing unit customizes the providing method based on the visitor's past feedback. This makes it possible to provide an AR experience based on the visitor's past feedback. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the visitor's past feedback data into the generating AI and cause the generating AI to customize the providing method.

[0053] When analyzing reactions, the analysis unit can select the optimal analysis method by referring to the visitor's past reaction history. The analysis unit, for example, selects the optimal analysis method based on the visitor's past reaction history. For example, the analysis unit analyzes the visitor's past reaction history and selects the optimal analysis method. The analysis unit can also select the optimal analysis method by referring to the visitor's past feedback. For example, the analysis unit selects the analysis method based on the visitor's past feedback. This makes it possible to analyze reactions based on the visitor's past reaction history. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the visitor's past reaction history data into the generation AI and have the generation AI execute the optimal analysis method.

[0054] When analyzing reactions, the analysis unit can customize the analysis content based on the visitor's current interests and areas of interest. The analysis unit, for example, performs reaction analysis based on themes in which the visitor is currently interested. For example, it performs related reaction analysis based on keywords recently searched by the visitor. The analysis unit can also perform reaction analysis based on the content of an event in which the visitor is currently participating. For example, it performs reaction analysis related to the event in which the visitor is currently participating. This makes it possible to perform reaction analysis according to the visitor's current interests and areas of interest. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input data related to the visitor's current interests and areas of interest into the generation AI and have the generation AI customize the analysis content.

[0055] The analysis unit can improve the analysis method by reflecting visitor feedback during reaction analysis. The analysis unit, for example, adjusts the content of the reaction analysis based on visitor feedback. For example, it analyzes visitor feedback and performs reaction analysis that emphasizes specific elements. The analysis unit can also select the optimal analysis method by referring to visitor feedback. For example, it improves the analysis method based on visitor feedback. This makes it possible to improve reaction analysis based on visitor feedback. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input visitor feedback data into a generation AI and have the generation AI improve the analysis method.

[0056] When analyzing reactions, the analysis unit can select the optimal analysis method by taking into account the visitor's geographical location information. The analysis unit, for example, performs reaction analysis related to the visitor's current location. For example, it performs reaction analysis related to the tourist spot where the visitor is currently located. The analysis unit can also perform reaction analysis related to tourist spots nearby the visitor. For example, it performs reaction analysis related to tourist spots nearby the visitor. Furthermore, the analysis unit can also perform reaction analysis related to places the visitor has visited in the past. For example, it performs reaction analysis related to tourist spots visited in the past. This makes it possible to analyze reactions based on the visitor's geographical location information. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the visitor's geographical location information data into the generation AI and have the generation AI execute the optimal analysis method.

[0057] During reaction analysis, the analysis unit can analyze the visitor's social media activity and suggest analysis content. For example, the analysis unit performs reaction analysis related to places where the visitor checked in on social media. For example, the analysis unit performs reaction analysis related to tourist spots where the visitor checked in on social media. The analysis unit can also analyze the content posted by the visitor on social media and perform related reaction analysis. For example, the analysis unit performs reaction analysis based on the content posted by the visitor. Furthermore, the analysis unit can also perform related reaction analysis with reference to the activities of the visitor's friends on social media. For example, the analysis unit performs reaction analysis related to places where the visitor's friends checked in on social media. This makes it possible to perform reaction analysis based on the visitor's social media activity. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the visitor's social media activity data into the generation AI and have the generation AI suggest analysis content.

[0058] The analysis unit can customize the analysis method by reflecting the visitor's past feedback when analyzing reactions. The analysis unit, for example, adjusts the content of the reaction analysis based on the visitor's past feedback. For example, it analyzes the visitor's past feedback and performs reaction analysis that emphasizes specific elements. The analysis unit can also select the optimal analysis method by referring to the visitor's past feedback. For example, it customizes the analysis method based on the visitor's past feedback. This makes it possible to analyze reactions based on the visitor's past feedback. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the visitor's past feedback data into the generation AI and have the generation AI customize the analysis method.

[0059] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0060] The determination unit can analyze the visitor's past visit history and select an appropriate level determination method. For example, it can analyze the history of tourist spots visited and events attended by the visitor in the past and select an appropriate level. The determination unit can also analyze the visitor's past feedback and select an optimal level. For example, it can adjust the level determination method based on feedback provided by the visitor in the past. This makes it possible to determine the level based on the visitor's past visit history. Some or all of the above-mentioned processing in the determination unit may be performed using, for example, AI, or may be performed without using AI. For example, the determination unit can input the visitor's past visit history data into the generation AI and have the generation AI perform level determination.

[0061] The storytelling unit can improve the accuracy of storytelling by referring to the visitor's past storytelling results. For example, the storytelling content can be adjusted based on information that the visitor has been interested in in the past. For example, the storytelling can be based on a theme that the visitor has shown interest in in the past. The storytelling unit can also analyze the visitor's past responses and select the optimal storytelling method. For example, the storytelling content can be adjusted based on the visitor's past response data. Furthermore, the storytelling unit can improve the accuracy of storytelling by referring to the visitor's past feedback. For example, the storytelling content can be adjusted based on the visitor's past feedback. This makes it possible to improve the accuracy of storytelling based on the visitor's past storytelling results. Some or all of the above-described processing in the storytelling unit may be performed using, or without, a generation AI. For example, the storytelling unit can input the visitor's past storytelling result data into the generation AI and have the generation AI improve the accuracy of storytelling.

[0062] When providing an AR experience, the provision unit can select the optimal provision method by referring to the visitor's past experience history. For example, the provision unit selects the optimal provision method based on the visitor's past AR experiences. For example, the provision unit selects the provision method based on the visitor's past AR experience history. The provision unit can also select the optimal provision method by referring to the visitor's past feedback. For example, the provision unit selects the provision method based on the visitor's past feedback. The provision unit can also analyze the visitor's past experience history and select the optimal provision method. For example, the provision unit selects the provision method based on the visitor's past experience history. This makes it possible to provide an AR experience based on the visitor's past experience history. Some or all of the above-described processing in the provision unit may be performed using, or without, AI. For example, the provision unit can input the visitor's past experience history data into the generation AI and cause the generation AI to execute the optimal provision method.

[0063] When analyzing reactions, the analysis unit can select the optimal analysis method by referring to the visitor's past reaction history. For example, the optimal analysis method is selected based on the visitor's past reaction history. For example, the optimal analysis method is selected by analyzing the visitor's past reaction history. The analysis unit can also select the optimal analysis method by referring to the visitor's past feedback. For example, the analysis unit selects the analysis method based on the visitor's past feedback. This makes it possible to analyze reactions based on the visitor's past reaction history. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the visitor's past reaction history data into the generation AI and have the generation AI execute the optimal analysis method.

[0064] The determination unit can analyze the visitor's social media activity and determine the relevant level. For example, it provides information related to places where the visitor has checked in on social media. For example, it determines the level based on information about tourist spots where the visitor has checked in on social media. The determination unit can also analyze the content of the visitor's posts on social media and determine the relevant level. For example, it determines the level based on the content of the visitor's posts. Furthermore, the determination unit can determine the relevant level by referring to the activities of the visitor's friends on social media. For example, it determines the level based on information about places where the visitor's friends have checked in on social media. This makes it possible to determine the level based on the visitor's social media activity. Some or all of the above-described processing by the determination unit may be performed using, for example, AI, or may be performed without using AI. For example, the determination unit can input the visitor's social media activity data into the generation AI and have the generation AI determine the level.

[0065] The processing flow of the first embodiment will be briefly explained below.

[0066] Step 1: The determination unit determines the level of the visitor. The visitor's level may be, for example, beginner, intermediate, or advanced. The determination unit may determine the level based on the visitor's age or interests. The determination unit may also estimate the visitor's emotions and adjust the level determination criteria based on the estimated emotions. For example, if the visitor is excited, a level that provides more detailed and stimulating information may be selected. Step 2: The storytelling unit uses a generation AI to tell a story based on the level determined by the determination unit. The storytelling is based on the structure of the story and the media used. For example, the generation AI uses a text generation AI (e.g., LLM) to tell a story according to the visitor's level. The storytelling unit can also estimate the visitor's emotions and adjust the way the storytelling is expressed based on the estimated emotions. For example, if the visitor is excited, it can perform storytelling that adds visually stimulating effects. Step 3: The providing unit provides an AR experience based on the storytelling provided by the storytelling unit. AR experiences include those provided through smartphones and tablets. For example, by pointing a camera at a specific location, information related to that location can be displayed. The providing unit can also estimate the visitor's emotions and adjust the way the AR experience is provided based on the estimated emotions. For example, if the visitor is excited, a visually stimulating AR experience can be provided. Step 4: The analysis unit analyzes the visitor's response to the AR experience provided by the provision unit. The visitor's response includes facial expressions, actions, feedback, etc. The analysis unit can analyze the visitor's response in real time and provide feedback to the storytelling unit. This can deepen the visitor's understanding and experience. For example, the content of the storytelling can be adjusted based on the visitor's response analyzed in real time.

[0067] (Example 2) A system according to an embodiment of the present invention allows visitors to understand and experience the culture and history of a region. The system determines the level of the visitor, performs storytelling based on that level, provides an AR experience, and analyzes the visitor's response. For example, the system uses AI that has learned information about the history and culture of a tourist destination to provide storytelling tailored to the visitor's level. Then, using AR technology, the system provides an experience that makes the visitor feel as if they are actually in the location. This allows visitors to gain a deeper understanding of the local culture and history and to have an engaging experience. This allows the system to deepen the visitor's understanding and experience. For example, when visiting a historical building, the visitor can learn more about the building's history and background. Also, when participating in a cultural event, the visitor can understand the significance and background of the event. This allows the visitor to more deeply appreciate the charm of the region.

[0068] A cultural experience system according to an embodiment includes a determination unit, a storytelling unit, a provision unit, and an analysis unit. The determination unit determines the level of a visitor. Examples of visitor levels include, but are not limited to, beginner, intermediate, and advanced. The determination unit can determine the level based on, for example, the visitor's age and interests. The determination unit can also estimate the visitor's emotions and adjust the level determination criteria based on the estimated emotions. For example, if the visitor is excited, a level that provides more detailed and stimulating information can be selected. The storytelling unit uses a generation AI to perform storytelling based on the level determined by the determination unit. Storytelling can be performed based on, for example, the structure of the story and the media used, but is not limited to, examples. For example, the generation AI uses a text generation AI (e.g., LLM) to perform storytelling according to the visitor's level. The storytelling unit can also estimate the visitor's emotions and adjust the way the storytelling is expressed based on the estimated emotions. For example, if the visitor is excited, storytelling can be performed with visually stimulating effects. The providing unit provides an AR experience based on the storytelling provided by the storytelling unit. AR experiences include, but are not limited to, those provided through a smartphone or tablet. For example, the providing unit can point a camera at a specific location and display information related to that location. The providing unit can also estimate a visitor's emotions and adjust how the AR experience is provided based on the estimated emotions. For example, if the visitor is excited, a visually stimulating AR experience can be provided. The analysis unit analyzes the visitor's reaction to the AR experience provided by the providing unit. The visitor's reaction can include, but is not limited to, facial expressions, actions, feedback, etc. For example, the analysis unit can analyze the visitor's reaction in real time and provide feedback to the storytelling unit. This allows the cultural experience system according to the embodiment to deepen the visitor's understanding and experience. For example, the content of the storytelling can be adjusted based on the visitor's reaction analyzed in real time.

[0069] The determination unit can determine the level based on the visitor's age or interests. The determination unit can determine the level based on, for example, the visitor's age. For example, the level can be set according to age groups such as children, adolescents, adults, and the elderly. The determination unit can also determine the level based on the visitor's interests. For example, the level can be set based on the visitor's hobbies, areas of interest, past behavioral history, etc. This makes it possible to determine the level according to the visitor's age and interests. Some or all of the above-described processing in the determination unit can be performed using, or without, AI, for example. For example, the determination unit can input data regarding the visitor's age and interests into the generation AI and have the generation AI determine the level.

[0070] The storytelling unit can perform storytelling according to the visitor's level. The storytelling unit, for example, adjusts the content of the storytelling according to the visitor's level. For example, the storytelling unit can explain things in simple terms to beginners, provide detailed information to intermediate visitors, and provide storytelling including specialized content to advanced visitors. The storytelling unit can also analyze the visitor's response in real time and adjust the content of the storytelling. For example, if the visitor shows interest, the storytelling unit can provide more detailed information, and if the visitor loses interest, the storytelling unit can switch to a simple explanation. This enables appropriate storytelling according to the visitor's level. Some or all of the above-described processing in the storytelling unit may be performed using, or without, a generation AI. For example, the storytelling unit can input data regarding the visitor's level into the generation AI and have the generation AI execute the storytelling content.

[0071] The providing unit can provide an AR experience through a smartphone or tablet. The providing unit provides the AR experience using, for example, a smartphone or tablet. For example, when a visitor points the smartphone camera at a specific location, information related to that location is displayed. The providing unit can also estimate the visitor's emotions and adjust the way the AR experience is provided based on the estimated emotions. For example, if the visitor is excited, a visually stimulating AR experience can be provided, and if the visitor is relaxed, a calm AR experience can be provided. This allows the visitor to enjoy the AR experience through the smartphone or tablet. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input data acquired by the smartphone or tablet into a generating AI and cause the generating AI to provide the AR experience.

[0072] The analysis unit can analyze the visitor's reactions in real time and provide feedback to the storytelling unit. For example, the analysis unit captures the visitor's facial expressions and behavior with a camera and analyzes them in real time. For example, the analysis unit calculates an emotion score based on changes in the visitor's facial expressions and provides feedback to the storytelling unit. The analysis unit can also record the visitor's voice and estimate their emotions using voice analysis technology. For example, the analysis unit can analyze the tone and speed of the visitor's voice and calculate an emotion score. Furthermore, the analysis unit can collect the visitor's biometric data (heart rate and electrodermal activity) using a sensor and estimate their emotions using an emotion estimation algorithm. For example, the analysis unit can calculate an emotion score based on heart rate fluctuations. This allows the visitor's reactions to be analyzed in real time and the content of the storytelling to be adjusted. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit can input image data of a visitor taken with a camera into the generation AI and have the generation AI estimate the visitor's emotions.

[0073] The providing unit can display information related to a specific location when a visitor points a camera at the location. For example, when a visitor points a smartphone or tablet camera at a specific location, the providing unit displays information related to the location. For example, when a visitor points a camera at a historical building, information about the building's history and background is displayed. The providing unit can also estimate the visitor's emotions and adjust the way the AR experience is provided based on the estimated emotions. For example, if the visitor is excited, a visually stimulating AR experience can be provided, and if the visitor is relaxed, a calming AR experience can be provided. This allows the visitor to obtain information related to the location by pointing the camera at the specific location. Some or all of the above-described processing by the providing unit may be performed using, for example, AI, or may be performed without AI. For example, the providing unit can input data acquired by the camera into a generating AI and cause the generating AI to display related information.

[0074] The determination unit can estimate the visitor's emotions and adjust the level determination criteria based on the estimated emotions. For example, the determination unit captures the visitor's facial expressions and behavior with a camera and estimates the emotions using an emotion estimation algorithm. For example, the determination unit calculates an emotion score based on changes in the visitor's facial expressions and adjusts the level determination criteria. The determination unit can also record the visitor's voice and estimate the emotion using voice analysis technology. For example, the determination unit analyzes the tone and speed of the visitor's voice, calculates an emotion score, and adjusts the level determination criteria. Furthermore, the determination unit can collect the visitor's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. For example, the determination unit calculates an emotion score based on heart rate fluctuations and adjusts the level determination criteria. This makes it possible to determine the level according to the visitor's emotions. Emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the determination unit may be performed using, for example, AI, or may be performed without using AI. For example, the determination unit may input image data of a visitor taken with a camera to the generation AI and cause the generation AI to estimate emotions.

[0075] The determination unit can analyze the visitor's past visit history and select an appropriate level determination method. For example, the determination unit selects a level that the visitor is likely to be interested in based on information about places the visitor has visited in the past. For example, the determination unit analyzes the history of tourist spots the visitor has visited and events the visitor has participated in in the past and selects a relevant level. The determination unit can also analyze the visitor's past feedback and select an optimal level. For example, the level determination method can be adjusted based on feedback the visitor has provided in the past. This makes it possible to determine the level based on the visitor's past visit history. Some or all of the above-mentioned processing in the determination unit may be performed using, for example, AI, or may be performed without using AI. For example, the determination unit can input the visitor's past visit history data into the generation AI and have the generation AI determine the level.

[0076] The determination unit can determine the level based on the visitor's current interests and areas of interest. The determination unit selects the level based on, for example, themes in which the visitor is currently interested. For example, the determination unit selects a relevant level based on keywords recently searched by the visitor or the content of events the visitor is attending. The determination unit can also determine the level based on the visitor's recent behavioral history and survey results. This makes it possible to determine the level according to the visitor's current interests and areas of interest. Some or all of the above-mentioned processing in the determination unit may be performed using, for example, AI, or may be performed without using AI. For example, the determination unit can input data on the visitor's current interests and areas of interest to the generation AI and have the generation AI determine the level.

[0077] The determination unit can select an appropriate level determination means depending on the visitor's input method. For example, if the visitor uses voice input, the determination unit determines the level based on voice analysis. For example, the determination unit analyzes the visitor's voice data and selects an appropriate level. Furthermore, if the visitor uses text input, the determination unit can also determine the level based on text analysis. For example, the determination unit analyzes the visitor's text data and selects an appropriate level. Furthermore, if the visitor uses image input, the determination unit can also determine the level based on image analysis. For example, the determination unit analyzes the visitor's image data and selects an appropriate level. This makes it possible to determine the level depending on the visitor's input method. Some or all of the above-mentioned processing in the determination unit may be performed using, for example, AI, or may be performed without using AI. For example, the determination unit can input the visitor's input data to a generation AI and have the generation AI determine the level.

[0078] The determination unit can estimate the visitor's emotions and determine the priority of the levels to be determined based on the estimated emotions. For example, the determination unit captures the visitor's facial expressions and behavior with a camera and estimates the emotions using an emotion estimation algorithm. For example, the determination unit calculates an emotion score based on changes in the visitor's facial expressions and determines the priority of the levels. The determination unit can also record the visitor's voice and estimate the emotion using voice analysis technology. For example, the determination unit can analyze the tone and speed of the visitor's voice to calculate an emotion score and determine the priority of the levels. Furthermore, the determination unit can collect the visitor's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. For example, the determination unit can calculate an emotion score based on heart rate fluctuations and determine the priority of the levels. This makes it possible to determine the priority of the levels according to the visitor's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the determination unit may be performed using, for example, AI, or may be performed without using AI. For example, the determination unit may input image data of a visitor taken with a camera to the generation AI and cause the generation AI to estimate emotions.

[0079] The determination unit can prioritize determining a level with a high relevance based on the visitor's geographical location information. For example, the determination unit can prioritize providing information related to the visitor's current location. For example, the determination unit can determine the level based on information related to the tourist spot where the visitor is currently located. The determination unit can also prioritize providing information related to tourist spots near the visitor. For example, the determination unit can determine the level based on information about tourist spots near the visitor. The determination unit can also prioritize providing information related to places the visitor has visited in the past. For example, the determination unit can determine the level based on information about tourist spots the visitor has visited in the past. This makes it possible to determine the level based on the visitor's geographical location information. Some or all of the above-described processing in the determination unit can be performed using, or without, AI. For example, the determination unit can input the visitor's geographical location information data to the generation AI and have the generation AI determine the level.

[0080] The determination unit can analyze the visitor's social media activity and determine the relevant level. For example, the determination unit provides information related to places where the visitor has checked in on social media. For example, the level is determined based on information about tourist spots where the visitor has checked in on social media. The determination unit can also analyze the content of the visitor's posts on social media and determine the relevant level. For example, the level is determined based on the content of the visitor's posts. Furthermore, the determination unit can determine the relevant level by referring to the activities of the visitor's friends on social media. For example, the level is determined based on information about places where the visitor's friends have checked in on social media. This makes it possible to determine the level based on the visitor's social media activity. Some or all of the above-described processing in the determination unit may be performed using, for example, AI, or may be performed without using AI. For example, the determination unit can input the visitor's social media activity data to the generation AI and have the generation AI determine the level.

[0081] The determination unit can customize the level determination method by reflecting the visitor's past feedback. The determination unit, for example, adjusts the level determination method based on feedback provided by the visitor in the past. For example, it analyzes the visitor's past feedback and provides a specific level preferentially. The determination unit can also select the optimal level determination method by referring to the visitor's past feedback. For example, it customizes the level determination method based on the visitor's past feedback. This makes it possible to determine the level based on the visitor's past feedback. Some or all of the above-described processing in the determination unit may be performed using AI, for example, or may be performed without using AI. For example, the determination unit can input the visitor's past feedback data into the generation AI and have the generation AI perform level determination.

[0082] The storytelling unit can estimate the visitor's emotions and adjust the storytelling method based on the estimated emotions. For example, the storytelling unit captures the visitor's facial expressions and behavior with a camera and estimates emotions using an emotion estimation algorithm. For example, the storytelling unit calculates an emotion score based on changes in the visitor's facial expressions and adjusts the storytelling method. The storytelling unit can also record the visitor's voice and estimate emotions using voice analysis technology. For example, the storytelling unit can analyze the tone and speed of the visitor's voice, calculate an emotion score, and adjust the storytelling method. Furthermore, the storytelling unit can collect the visitor's biometric data (heart rate and electrodermal activity) with a sensor and estimate emotions using an emotion estimation algorithm. For example, the storytelling unit can calculate an emotion score based on fluctuations in heart rate and adjust the storytelling method. This enables the storytelling method to be tailored to the visitor's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the storytelling unit may be performed using AI, or may be performed without using AI. For example, the storytelling unit may input image data of a visitor taken with a camera into the generation AI and have the generation AI estimate emotions.

[0083] The storytelling unit can adjust the level of detail in the storytelling based on the importance of the information. For example, the storytelling unit provides detailed explanations for important historical events. For example, detailed storytelling is performed for information in which visitors are particularly interested. The storytelling unit can also provide brief explanations for general information. For example, brief storytelling is performed for information in which visitors are not particularly interested. This enables the level of detail in the storytelling to be adjusted according to the importance of the information. Some or all of the above-mentioned processing in the storytelling unit may be performed using, or without, the generation AI. For example, the storytelling unit can input data regarding the importance of the information into the generation AI and have the generation AI execute the level of detail in the storytelling.

[0084] The storytelling unit can apply different storytelling algorithms depending on the category of information. For example, for historical information, the storytelling unit performs storytelling in chronological order. For example, if a visitor is interested in historical events, the storytelling unit performs storytelling in chronological order. The storytelling unit can also perform storytelling by theme for cultural information. For example, if a visitor is interested in cultural events, the storytelling unit can perform storytelling by theme. The storytelling unit can also perform storytelling by location for tourist attraction information. For example, if a visitor is interested in a particular tourist attraction, the storytelling unit can perform storytelling by location. This enables storytelling according to the category of information. Some or all of the above-described processing in the storytelling unit may be performed using, or without, a generation AI. For example, the storytelling unit can input data regarding the category of information into the generation AI and cause the generation AI to apply the storytelling algorithm.

[0085] The storytelling unit can improve the accuracy of storytelling by referring to the visitor's past storytelling results. The storytelling unit, for example, adjusts the content of the storytelling based on information that the visitor has been interested in in the past. For example, the storytelling is performed based on a theme that the visitor has shown interest in in the past. The storytelling unit can also analyze the visitor's past responses and select the optimal storytelling method. For example, the storytelling content can be adjusted based on the visitor's past response data. Furthermore, the storytelling unit can improve the accuracy of storytelling by referring to the visitor's past feedback. For example, the storytelling content can be adjusted based on the visitor's past feedback. This makes it possible to improve the accuracy of storytelling based on the visitor's past storytelling results. Some or all of the above-described processing in the storytelling unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the storytelling unit can input the visitor's past storytelling result data into the generation AI and cause the generation AI to improve the accuracy of storytelling.

[0086] The storytelling unit can estimate the visitor's emotions and adjust the length of the storytelling based on the estimated emotions. For example, the storytelling unit captures the visitor's facial expressions and behavior with a camera and estimates the emotions using an emotion estimation algorithm. For example, the storytelling unit calculates an emotion score based on changes in the visitor's facial expressions and adjusts the length of the storytelling. The storytelling unit can also record the visitor's voice and estimate the emotions using voice analysis technology. For example, the storytelling unit analyzes the tone and speed of the visitor's voice, calculates an emotion score, and adjusts the length of the storytelling. Furthermore, the storytelling unit can collect the visitor's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotions using an emotion estimation algorithm. For example, the storytelling unit calculates an emotion score based on heart rate fluctuations and adjusts the length of the storytelling. This enables the length of the storytelling to be adjusted according to the visitor's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the storytelling unit may be performed using, for example, AI, or may be performed without using AI. For example, the storytelling unit may input image data of a visitor taken with a camera into the generation AI and have the generation AI estimate the visitor's emotions.

[0087] The storytelling unit can determine the priority of storytelling based on the time when information is provided. For example, the storytelling unit can provide important information first to attract the visitor's interest. For example, information that is likely to be of particular interest to the visitor can be provided first. The storytelling unit can also postpone general information and prioritize providing important information. For example, information that the visitor is not particularly interested in can be postponed. Furthermore, the storytelling unit can provide information that is likely to be of interest to the visitor first to continue to attract their interest. For example, information that is likely to be of interest to the visitor can be provided preferentially. This enables the priority of storytelling to be determined according to the time when information is provided. Some or all of the above-described processing in the storytelling unit may be performed using, or without, the generation AI. For example, the storytelling unit can input data regarding the time when information is provided to the generation AI and have the generation AI execute the storytelling priority.

[0088] The storytelling unit can adjust the order of storytelling based on the relevance of the information. For example, the storytelling unit may provide related information in succession to deepen the visitor's understanding. For example, a visitor's understanding deepens when they receive related information in succession. The storytelling unit can also postpone less relevant information and prioritize providing important information. For example, it may postpone information that the visitor is less interested in. Furthermore, the storytelling unit can provide information that is likely to interest the visitor first to keep them interested. For example, it prioritizes providing information that is likely to interest the visitor. This enables the order of storytelling to be based on the relevance of the information. Some or all of the above-described processing in the storytelling unit may be performed using, or without, a generation AI. For example, the storytelling unit may input data regarding the relevance of the information into the generation AI and have the generation AI execute the order of storytelling.

[0089] The storytelling unit can adjust the use of technical terms in the storytelling according to the visitor's level of expertise. For example, the storytelling unit uses simple language to explain things to visitors with little expertise. For example, if the visitor is a beginner, the storytelling unit uses simple language to explain things. The storytelling unit can also use detailed technical terms to explain things to visitors with expertise. For example, if the visitor is an expert, the storytelling unit uses detailed technical terms to explain things. Furthermore, the storytelling unit can adjust the use of technical terms according to the visitor's level of understanding. For example, the use of technical terms is adjusted according to the visitor's level of understanding. This enables storytelling according to the visitor's level of expertise. Some or all of the above-mentioned processing in the storytelling unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the storytelling unit can input data regarding the visitor's level of expertise into the generation AI and have the generation AI execute the use of technical terms.

[0090] The provision unit can estimate the visitor's emotions and adjust the method of providing the AR experience based on the estimated emotions. For example, the provision unit captures the visitor's facial expressions and behavior with a camera and estimates the emotions using an emotion estimation algorithm. For example, the provision unit calculates an emotion score based on changes in the visitor's facial expressions and adjusts the method of providing the AR experience. The provision unit can also record the visitor's voice and estimate the emotions using voice analysis technology. For example, the provision unit analyzes the tone and speed of the visitor's voice, calculates an emotion score, and adjusts the method of providing the AR experience. Furthermore, the provision unit can collect the visitor's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotions using an emotion estimation algorithm. For example, the provision unit calculates an emotion score based on heart rate fluctuations and adjusts the method of providing the AR experience. This enables the provision of an AR experience based on the visitor's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit may input image data of a visitor taken with a camera to the generating AI and cause the generating AI to estimate emotions.

[0091] When providing an AR experience, the provision unit can select the optimal provision method by referring to the visitor's past experience history. For example, the provision unit selects the optimal provision method based on the visitor's past AR experiences. For example, the provision unit selects the provision method based on the visitor's past AR experience history. The provision unit can also select the optimal provision method by referring to the visitor's past feedback. For example, the provision unit selects the provision method based on the visitor's past feedback. Furthermore, the provision unit can analyze the visitor's past experience history and select the optimal provision method. For example, the provision unit selects the provision method based on the visitor's past experience history. This makes it possible to provide an AR experience based on the visitor's past experience history. Some or all of the above-described processing in the provision unit may be performed using, for example, AI, or may be performed without using AI. For example, the provision unit can input the visitor's past experience history data into the generation AI and cause the generation AI to execute the optimal provision method.

[0092] When providing an AR experience, the provision unit can customize the content to be provided based on the visitor's current interests and areas of interest. For example, the provision unit provides an AR experience based on a theme that the visitor is currently interested in. For example, the provision unit can provide a related AR experience based on keywords recently searched by the visitor. The provision unit can also provide an AR experience based on the content of an event in which the visitor is currently participating. For example, the provision unit can provide an AR experience related to the event in which the visitor is currently participating. This makes it possible to provide an AR experience that suits the visitor's current interests and areas of interest. Some or all of the above-described processing by the provision unit may be performed using, or without, AI. For example, the provision unit can input data related to the visitor's current interests and areas of interest into a generation AI and cause the generation AI to customize the content to be provided.

[0093] The provision unit can improve the provision method by reflecting visitor feedback when providing an AR experience. The provision unit, for example, adjusts the content of the AR experience based on visitor feedback. For example, the provision unit analyzes visitor feedback and provides an AR experience that emphasizes specific elements. The provision unit can also select the optimal provision method by referring to visitor feedback. For example, the provision method can be improved based on visitor feedback. This makes it possible to improve the provision method of the AR experience based on visitor feedback. Some or all of the above-mentioned processing in the provision unit may be performed using AI, for example, or may be performed without using AI. For example, the provision unit can input visitor feedback data into a generation AI and cause the generation AI to improve the provision method.

[0094] The provision unit can estimate the visitor's emotions and determine the priority of AR experiences based on the estimated emotions. For example, the provision unit captures the visitor's facial expressions and behavior with a camera and estimates the emotions using an emotion estimation algorithm. For example, the provision unit calculates an emotion score based on changes in the visitor's facial expressions and determines the priority of AR experiences. The provision unit can also record the visitor's voice and estimate the emotion using voice analysis technology. For example, the provision unit can analyze the tone and speed of the visitor's voice, calculate an emotion score, and determine the priority of AR experiences. Furthermore, the provision unit can collect the visitor's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. For example, the provision unit can calculate an emotion score based on heart rate fluctuations and determine the priority of AR experiences. This enables the priority of AR experiences to be determined according to the visitor's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit may input image data of a visitor taken with a camera to the generating AI and cause the generating AI to estimate emotions.

[0095] When providing an AR experience, the providing unit can select the optimal providing method by taking into consideration the visitor's geographical location information. The providing unit, for example, provides an AR experience related to the location where the visitor is currently located. For example, it provides an AR experience related to a tourist spot where the visitor is currently located. The providing unit can also provide an AR experience related to a tourist spot near the visitor. For example, it can provide an AR experience related to a tourist spot near the visitor. Furthermore, the providing unit can also provide an AR experience related to a location that the visitor has visited in the past. For example, it can provide an AR experience related to a tourist spot that the visitor has visited in the past. This makes it possible to provide an AR experience based on the visitor's geographical location information. Some or all of the above-described processing by the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input the visitor's geographical location information data to the generation AI and cause the generation AI to execute the optimal providing method.

[0096] When providing an AR experience, the providing unit can analyze the visitor's social media activity and suggest content to be provided. For example, the providing unit can provide an AR experience related to a place where the visitor has checked in on social media. For example, the providing unit can provide an AR experience related to a tourist spot where the visitor has checked in on social media. The providing unit can also analyze the visitor's social media posts and provide a related AR experience. For example, the providing unit can provide an AR experience based on the visitor's posts. Furthermore, the providing unit can provide a related AR experience by referring to the activities of the visitor's friends on social media. For example, the providing unit can provide an AR experience related to a place where the visitor's friends have checked in on social media. This makes it possible to provide an AR experience based on the visitor's social media activity. Some or all of the above-described processing by the providing unit may be performed using, or without, AI. For example, the providing unit can input the visitor's social media activity data into a generation AI and cause the generation AI to suggest content to be provided.

[0097] When providing an AR experience, the providing unit can customize the providing method by reflecting the visitor's past feedback. The providing unit, for example, adjusts the content of the AR experience based on the visitor's past feedback. For example, the providing unit analyzes the visitor's past feedback and provides an AR experience that emphasizes specific elements. The providing unit can also select the optimal providing method by referring to the visitor's past feedback. For example, the providing unit customizes the providing method based on the visitor's past feedback. This makes it possible to provide an AR experience based on the visitor's past feedback. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the visitor's past feedback data into the generating AI and cause the generating AI to customize the providing method.

[0098] The analysis unit can estimate the visitor's emotions and adjust the reaction analysis method based on the estimated emotions. For example, the analysis unit captures the visitor's facial expressions and behavior with a camera and estimates emotions using an emotion estimation algorithm. For example, the analysis unit calculates an emotion score based on changes in the visitor's facial expressions and adjusts the reaction analysis method. The analysis unit can also record the visitor's voice and estimate emotions using voice analysis technology. For example, the analysis unit analyzes the tone and speed of the visitor's voice, calculates an emotion score, and adjusts the reaction analysis method. Furthermore, the analysis unit can collect the visitor's biometric data (heart rate and electrodermal activity) with a sensor and estimate emotions using an emotion estimation algorithm. For example, the analysis unit calculates an emotion score based on heart rate fluctuations and adjusts the reaction analysis method. This enables reaction analysis based on the visitor's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit may input image data of a visitor taken with a camera into the generation AI and have the generation AI estimate the visitor's emotions.

[0099] When analyzing reactions, the analysis unit can select the optimal analysis method by referring to the visitor's past reaction history. The analysis unit, for example, selects the optimal analysis method based on the visitor's past reaction history. For example, the analysis unit analyzes the visitor's past reaction history and selects the optimal analysis method. The analysis unit can also select the optimal analysis method by referring to the visitor's past feedback. For example, the analysis unit selects the analysis method based on the visitor's past feedback. This makes it possible to analyze reactions based on the visitor's past reaction history. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the visitor's past reaction history data into the generation AI and have the generation AI execute the optimal analysis method.

[0100] When analyzing reactions, the analysis unit can customize the analysis content based on the visitor's current interests and areas of interest. The analysis unit, for example, performs reaction analysis based on themes in which the visitor is currently interested. For example, it performs related reaction analysis based on keywords recently searched by the visitor. The analysis unit can also perform reaction analysis based on the content of an event in which the visitor is currently participating. For example, it performs reaction analysis related to the event in which the visitor is currently participating. This makes it possible to perform reaction analysis according to the visitor's current interests and areas of interest. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input data related to the visitor's current interests and areas of interest into the generation AI and have the generation AI customize the analysis content.

[0101] The analysis unit can improve the analysis method by reflecting visitor feedback during reaction analysis. The analysis unit, for example, adjusts the content of the reaction analysis based on visitor feedback. For example, it analyzes visitor feedback and performs reaction analysis that emphasizes specific elements. The analysis unit can also select the optimal analysis method by referring to visitor feedback. For example, it improves the analysis method based on visitor feedback. This makes it possible to improve reaction analysis based on visitor feedback. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input visitor feedback data into a generation AI and have the generation AI improve the analysis method.

[0102] The analysis unit can estimate the visitor's emotions and determine the priority of reaction analysis based on the estimated emotions. For example, the analysis unit captures the visitor's facial expressions and behavior with a camera and estimates the emotions using an emotion estimation algorithm. For example, the analysis unit calculates an emotion score based on changes in the visitor's facial expressions and determines the priority of reaction analysis. The analysis unit can also record the visitor's voice and estimate the emotion using voice analysis technology. For example, the analysis unit analyzes the tone and speed of the visitor's voice, calculates an emotion score, and determines the priority of reaction analysis. Furthermore, the analysis unit can collect the visitor's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. For example, the analysis unit calculates an emotion score based on heart rate fluctuations and determines the priority of reaction analysis. This enables the priority of reaction analysis to be determined according to the visitor's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit may input image data of a visitor taken with a camera into the generation AI and have the generation AI estimate the visitor's emotions.

[0103] When analyzing reactions, the analysis unit can select the optimal analysis method by taking into account the visitor's geographical location information. The analysis unit, for example, performs reaction analysis related to the visitor's current location. For example, it performs reaction analysis related to the tourist spot where the visitor is currently located. The analysis unit can also perform reaction analysis related to tourist spots nearby the visitor. For example, it performs reaction analysis related to tourist spots nearby the visitor. Furthermore, the analysis unit can also perform reaction analysis related to places the visitor has visited in the past. For example, it performs reaction analysis related to tourist spots visited in the past. This makes it possible to analyze reactions based on the visitor's geographical location information. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the visitor's geographical location information data into the generation AI and have the generation AI execute the optimal analysis method.

[0104] During reaction analysis, the analysis unit can analyze the visitor's social media activity and suggest analysis content. For example, the analysis unit performs reaction analysis related to places where the visitor checked in on social media. For example, the analysis unit performs reaction analysis related to tourist spots where the visitor checked in on social media. The analysis unit can also analyze the content posted by the visitor on social media and perform related reaction analysis. For example, the analysis unit performs reaction analysis based on the content posted by the visitor. Furthermore, the analysis unit can also perform related reaction analysis with reference to the activities of the visitor's friends on social media. For example, the analysis unit performs reaction analysis related to places where the visitor's friends checked in on social media. This makes it possible to perform reaction analysis based on the visitor's social media activity. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the visitor's social media activity data into the generation AI and have the generation AI suggest analysis content.

[0105] The analysis unit can customize the analysis method by reflecting the visitor's past feedback when analyzing reactions. The analysis unit, for example, adjusts the content of the reaction analysis based on the visitor's past feedback. For example, it analyzes the visitor's past feedback and performs reaction analysis that emphasizes specific elements. The analysis unit can also select the optimal analysis method by referring to the visitor's past feedback. For example, it customizes the analysis method based on the visitor's past feedback. This makes it possible to analyze reactions based on the visitor's past feedback. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the visitor's past feedback data into the generation AI and have the generation AI customize the analysis method. === Hard Collateral 1-1 === Each of the multiple elements including the above-mentioned determination unit, storytelling unit, provision unit, and analysis unit is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the determination unit is realized by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing device 12. For example, the storytelling unit is realized by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing device 12. For example, the provision unit is realized by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing device 12. For example, the analysis unit is realized by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing device 12. === Hard Collateral 1-2 === Each of the multiple elements including the above-mentioned determination unit, storytelling unit, provision unit, and analysis unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the determination unit is realized by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing device 12. For example, the storytelling unit is realized by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing device 12. For example, the provision unit is realized by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing device 12. For example, the analysis unit is realized by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing device 12. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned determination unit, storytelling unit, provision unit, and analysis unit is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the determination unit is realized by the control unit 46A of the headset type terminal 314 or the specific processing unit 290 of the data processing device 12. For example, the storytelling unit is realized by the control unit 46A of the headset type terminal 314 or the specific processing unit 290 of the data processing device 12. For example, the provision unit is realized by the control unit 46A of the headset type terminal 314 or the specific processing unit 290 of the data processing device 12. For example, the analysis unit is realized by the control unit 46A of the headset type terminal 314 or the specific processing unit 290 of the data processing device 12. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned determination unit, storytelling unit, provision unit, and analysis unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the determination unit is realized by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing device 12. For example, the storytelling unit is realized by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing device 12. For example, the provision unit is realized by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing device 12. For example, the analysis unit is realized by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing device 12.

[0106] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0107] The determination unit can analyze the visitor's past visit history and select an appropriate level determination method. For example, it can analyze the history of tourist spots visited and events attended by the visitor in the past and select an appropriate level. The determination unit can also analyze the visitor's past feedback and select an optimal level. For example, it can adjust the level determination method based on feedback provided by the visitor in the past. This makes it possible to determine the level based on the visitor's past visit history. Some or all of the above-mentioned processing in the determination unit may be performed using, for example, AI, or may be performed without using AI. For example, the determination unit can input the visitor's past visit history data into the generation AI and have the generation AI perform level determination.

[0108] The storytelling unit can improve the accuracy of storytelling by referring to the visitor's past storytelling results. For example, the storytelling content can be adjusted based on information that the visitor has been interested in in the past. For example, the storytelling can be based on a theme that the visitor has shown interest in in the past. The storytelling unit can also analyze the visitor's past responses and select the optimal storytelling method. For example, the storytelling content can be adjusted based on the visitor's past response data. Furthermore, the storytelling unit can improve the accuracy of storytelling by referring to the visitor's past feedback. For example, the storytelling content can be adjusted based on the visitor's past feedback. This makes it possible to improve the accuracy of storytelling based on the visitor's past storytelling results. Some or all of the above-described processing in the storytelling unit may be performed using, or without, a generation AI. For example, the storytelling unit can input the visitor's past storytelling result data into the generation AI and have the generation AI improve the accuracy of storytelling.

[0109] When providing an AR experience, the provision unit can select the optimal provision method by referring to the visitor's past experience history. For example, the provision unit selects the optimal provision method based on the visitor's past AR experiences. For example, the provision unit selects the provision method based on the visitor's past AR experience history. The provision unit can also select the optimal provision method by referring to the visitor's past feedback. For example, the provision unit selects the provision method based on the visitor's past feedback. The provision unit can also analyze the visitor's past experience history and select the optimal provision method. For example, the provision unit selects the provision method based on the visitor's past experience history. This makes it possible to provide an AR experience based on the visitor's past experience history. Some or all of the above-described processing in the provision unit may be performed using, or without, AI. For example, the provision unit can input the visitor's past experience history data into the generation AI and cause the generation AI to execute the optimal provision method.

[0110] When analyzing reactions, the analysis unit can select the optimal analysis method by referring to the visitor's past reaction history. For example, the optimal analysis method is selected based on the visitor's past reaction history. For example, the optimal analysis method is selected by analyzing the visitor's past reaction history. The analysis unit can also select the optimal analysis method by referring to the visitor's past feedback. For example, the analysis unit selects the analysis method based on the visitor's past feedback. This makes it possible to analyze reactions based on the visitor's past reaction history. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the visitor's past reaction history data into the generation AI and have the generation AI execute the optimal analysis method.

[0111] The determination unit can analyze the visitor's social media activity and determine the relevant level. For example, it provides information related to places where the visitor has checked in on social media. For example, it determines the level based on information about tourist spots where the visitor has checked in on social media. The determination unit can also analyze the content of the visitor's posts on social media and determine the relevant level. For example, it determines the level based on the content of the visitor's posts. Furthermore, the determination unit can determine the relevant level by referring to the activities of the visitor's friends on social media. For example, it determines the level based on information about places where the visitor's friends have checked in on social media. This makes it possible to determine the level based on the visitor's social media activity. Some or all of the above-described processing by the determination unit may be performed using, for example, AI, or may be performed without using AI. For example, the determination unit can input the visitor's social media activity data into the generation AI and have the generation AI determine the level.

[0112] The determination unit can estimate the visitor's emotions and adjust the level determination criteria based on the estimated emotions. For example, the visitor's facial expressions and behaviors are captured with a camera and the emotion is estimated using an emotion estimation algorithm. For example, the emotion score is calculated based on changes in the visitor's facial expressions and the level determination criteria are adjusted. The determination unit can also record the visitor's voice and estimate the emotion using voice analysis technology. For example, the tone and speed of the visitor's voice are analyzed to calculate the emotion score and adjust the level determination criteria. Furthermore, the determination unit can collect the visitor's biometric data (heart rate and electrodermal activity) using a sensor and estimate the emotion using an emotion estimation algorithm. For example, the emotion score is calculated based on heart rate fluctuations and the level determination criteria are adjusted. This enables the level determination to be based on the visitor's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-mentioned processing in the determination unit can be performed using, for example, AI, or without AI. For example, the judgment unit can input image data of a visitor taken with a camera into the generation AI and have the generation AI estimate emotions.

[0113] The storytelling unit can estimate the visitor's emotions and adjust the storytelling method based on the estimated emotions. For example, the visitor's facial expressions and behaviors are captured with a camera and the emotion estimation algorithm is used to estimate the emotion. For example, the emotion score is calculated based on changes in the visitor's facial expressions, and the storytelling method is adjusted accordingly. The storytelling unit can also record the visitor's voice and estimate the emotion using voice analysis technology. For example, the tone and speed of the visitor's voice are analyzed, an emotion score is calculated, and the storytelling method is adjusted accordingly. Furthermore, the storytelling unit can collect the visitor's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. For example, an emotion score is calculated based on heart rate fluctuations, and the storytelling method is adjusted accordingly. This enables the storytelling method to be tailored to the visitor's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the storytelling unit may be performed using, for example, AI, or may be performed without using AI. For example, the storytelling unit may input image data of a visitor taken with a camera into the generation AI and have the generation AI estimate the visitor's emotions.

[0114] The provision unit can estimate the visitor's emotions and adjust the method of providing the AR experience based on the estimated emotions. For example, the visitor's facial expressions and behaviors are captured with a camera and the emotion is estimated using an emotion estimation algorithm. For example, the emotion score is calculated based on changes in the visitor's facial expressions, and the method of providing the AR experience is adjusted. The provision unit can also record the visitor's voice and estimate the emotion using voice analysis technology. For example, the tone and speed of the visitor's voice are analyzed, the emotion score is calculated, and the method of providing the AR experience is adjusted. Furthermore, the provision unit can collect the visitor's biometric data (heart rate and electrodermal activity) using a sensor and estimate the emotion using an emotion estimation algorithm. For example, the emotion score is calculated based on heart rate fluctuations, and the method of providing the AR experience is adjusted. This enables the AR experience to be provided in accordance with the visitor's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit may input image data of a visitor taken with a camera to the generating AI and cause the generating AI to estimate emotions.

[0115] The analysis unit can estimate the visitor's emotions and adjust the reaction analysis method based on the estimated emotions. For example, the visitor's facial expressions and behaviors are captured with a camera and the emotion is estimated using an emotion estimation algorithm. For example, an emotion score is calculated based on changes in the visitor's facial expressions, and the reaction analysis method is adjusted. The analysis unit can also record the visitor's voice and estimate the emotion using voice analysis technology. For example, the tone and speed of the visitor's voice are analyzed, an emotion score is calculated, and the reaction analysis method is adjusted. Furthermore, the analysis unit can collect the visitor's biometric data (heart rate and electrodermal activity) using a sensor and estimate the emotion using an emotion estimation algorithm. For example, an emotion score is calculated based on heart rate fluctuations, and the reaction analysis method is adjusted. This enables reaction analysis based on the visitor's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit may input image data of a visitor taken with a camera into the generation AI and have the generation AI estimate the visitor's emotions.

[0116] The provision unit can estimate the visitor's emotions and prioritize AR experiences based on the estimated emotions. For example, the facility captures the visitor's facial expressions and behavior with a camera and estimates the emotions using an emotion estimation algorithm. For example, the facility calculates an emotion score based on changes in the visitor's facial expressions and prioritizes AR experiences. The provision unit can also record the visitor's voice and estimate emotions using voice analysis technology. For example, the facility analyzes the tone and speed of the visitor's voice to calculate an emotion score and prioritize AR experiences. The provision unit can also collect the visitor's biometric data (heart rate and electrodermal activity) using a sensor and estimate emotions using an emotion estimation algorithm. For example, the facility calculates an emotion score based on heart rate fluctuations and prioritizes AR experiences. This enables prioritization of AR experiences according to the visitor's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit may input image data of a visitor taken with a camera to the generating AI and cause the generating AI to estimate emotions.

[0117] The processing flow of the second embodiment will be briefly explained below.

[0118] Step 1: The determination unit determines the level of the visitor. The visitor's level may be, for example, beginner, intermediate, or advanced. The determination unit may determine the level based on the visitor's age or interests. The determination unit may also estimate the visitor's emotions and adjust the level determination criteria based on the estimated emotions. For example, if the visitor is excited, a level that provides more detailed and stimulating information may be selected. Step 2: The storytelling unit uses a generation AI to tell a story based on the level determined by the determination unit. The storytelling is based on the structure of the story and the media used. For example, the generation AI uses a text generation AI (e.g., LLM) to tell a story according to the visitor's level. The storytelling unit can also estimate the visitor's emotions and adjust the way the storytelling is expressed based on the estimated emotions. For example, if the visitor is excited, it can perform storytelling that adds visually stimulating effects. Step 3: The providing unit provides an AR experience based on the storytelling provided by the storytelling unit. AR experiences include those provided through smartphones and tablets. For example, by pointing a camera at a specific location, information related to that location can be displayed. The providing unit can also estimate the visitor's emotions and adjust the way the AR experience is provided based on the estimated emotions. For example, if the visitor is excited, a visually stimulating AR experience can be provided. Step 4: The analysis unit analyzes the visitor's response to the AR experience provided by the provision unit. The visitor's response includes facial expressions, actions, feedback, etc. The analysis unit can analyze the visitor's response in real time and provide feedback to the storytelling unit. This can deepen the visitor's understanding and experience. For example, the content of the storytelling can be adjusted based on the visitor's response analyzed in real time.

[0119] 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.

[0120] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<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.

[0121] 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.

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

[0123] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0124] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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).

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

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

[0139] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0140] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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).

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification 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 identification processing unit 290 using these models.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0155] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0156] 7, a 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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).

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also 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 perform the same process as the identification processing unit 290 using these models.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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).

[0176] 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.

[0177] 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."

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] [Explanation of symbols]

[0191] 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. a determination unit for determining the level of a visitor; a storytelling unit that performs storytelling based on the level determined by the determination unit; a providing unit that provides an AR experience based on the storytelling provided by the storytelling unit; an analysis unit that analyzes a visitor's response to the AR experience provided by the provision unit; A system comprising:

2. The determination unit Determine levels based on visitor age or interests 2. The system of claim 1.

3. The storytelling department Telling stories at the visitor's level 2. The system of claim 1.

4. The providing unit Deliver an AR experience through your smartphone or tablet 2. The system of claim 1.

5. The analysis unit Analyze visitors' reactions in real time and provide feedback to the storytelling department 2. The system of claim 1.

6. The providing unit Point the camera at a specific location to view information related to that location 2. The system of claim 1.

7. The determination unit Estimate the visitor's emotions and adjust the level criteria based on the estimated visitor emotions 2. The system of claim 1.

8. The determination unit Analyze the visitor's past visit history and select the appropriate level assessment method 2. The system of claim 1.

Images